In the last decade, various foods have been reformulated with plant protein ingredients to enhance plant-based food intake in our diet. Pulses are in the forefront as protein-rich sources to aid in providing sufficient daily protein intake and may be used as binders to reduce meat protein in product formulations. Pulses are seen as clean-label ingredients that bring benefits to meat products beyond protein content. Pulse flours may need pre-treatments because their endogenous bioactive components may not always be beneficial to meat products. Infrared (IR) treatment is a highly energy-efficient and environmentally friendly method of heating foods, creating diversity in plant-based ingredient functionality. This review discusses using IR-heating technology to modify the properties of pulses and their usefulness in comminuted meat products, with a major emphasis on lentils. IR heating enhances liquid-binding and emulsifying properties, inactivates oxidative enzymes, reduces antinutritional factors, and protects antioxidative properties of pulses. Meat products benefit from IR-treated pulse ingredients, showing improvements in product yields, oxidative stability, and nutrient availability while maintaining desired texture. IR-treated lentil-based ingredients, in particular, also enhance the raw color stability of beef burgers. Therefore, developing pulse-enriched meat products will be a viable approach toward the sustainable production of meat products.  It is necessary for people to have access to sufficient food that is safe, nutritious, and affordable and that is produced using environmentally responsible and sustainable production systems. Several dietary surveys have also shown the widespread prevalence of the suboptimal intake of protein and micronutrients for certain segments of the world population ,2,3,4. TProteins in the diet and their sources have evolved over time. In the beginning, slaughtered animals were used as entire carcasses or whole meat cuts, and this eventually led to their use in processed meat products to achieve full utilization of the meat and byproducts such as organs and other tissues. Prolongation of the shelf life as well as minimizing wastage through the ability to use various animal carcass parts in processed meat provides a significant advantage in the food basket of many civilizations and cultures. Such products are still popular because of their perfect merge with safe, tasteful, and convenient ready-to-eat protein-rich food in today’s fast-paced lifestyle. Owing to the dominance of existing fast-food practices, the global market demand for processed meats grew at a compound annual growth rate (CAGR) of 6.5% (2017–2021) and is expected to rise 6.3% (2022–2032), reaching a total of USD 605.14 billion in 2032 [As the world’s population grows, so does the demand for animal protein in which a considerable portion is satisfied with processed meat products. Some evidence suggests that the intake of red meat and extensively processed meat is associated with an increased risk of chronic illnesses such as cardiovascular diseases, type 2 diabetes, and some forms of cancer ,8. ThereThe reformulation of conventional meat products incorporating plant-based ingredients offers the meat industry several opportunities that benefit both the processor and the consumer. These include developing healthier products with more natural, functional ingredients; reducing waste generation while maximizing the total carcass utilization; diversification of product categories with new and improved hedonics; and lowering production costs to increase consumer affordability. The hundreds of different types of comminuted meat products available worldwide vary based on the method of manufacturing and composition of ingredients used in them. In an 1899 book on sausage making, Duff  and are According to Vatanparast et al. , CanadiaUtilizing pulses and pulse flours in meat product formulations presents some challenges that need to be addressed at the ingredient and/or product manufacturing stage. Pulses can bring in minor compounds that are nutritionally undesirable, incompatible techno-functional properties, off-flavors, and prooxidants that hinder their application in meat products. Among the various thermal treatments of pulses to improve their usability as ingredients for various food products, infrared (IR) heating has been under scientific investigation over the last decade to overcome these limitations, and the findings appear promising ,17,18. IThe aim of the present review is to bring together the literature on the addition of pulse-based ingredients to comminuted meat products and, in particular, those of IR-treated pulse ingredients in meat processing. The studies described in this review were from author collections and also found through database searches of Web of Science using search terms such as “pulse, lentil, pea, bean” and “meat, beef, chicken, pork, sausage, burger, patty, meatball” and “infrared, micronization, heat-treated” over the last 20 years. Taking into consideration all the pertinent research conducted on the application of IR-treated pulses in meat products, more than 80% has focused on lentil-derived products. Therefore, this review emphasizes the contribution of IR-treated lentils on the techno-functional and sensory aspects of comminuted meat products. A common definition of processed meat is “meat that has been transformed through salting, curing, fermentation, smoking, or other processes to enhance flavor or improve preservation” . ProcessWhole muscle products are made from large pieces or whole intact cuts of meat that are often molded and primarily seasoned, heat-processed, and smoked, while comminuted meat products are made from small pieces of meat that have been ground, minced, or chopped and often include a combination of meat and non-meat components . The earOne of the benefits of manufacturing comminuted meat products is that it allows for the utilization of lower-value meat, which can then be turned into even more valuable products. Meat product manufacturers use meat trimmings from inexpensive parts of the carcass, such as the head and cheeks, as well as mechanically separated meat (MSM), in the formulation of comminuted meat products. MSM is a cost-effective method of utilizing the last traces of meat from the carcass. Therefore, the manufacturing of comminuted meat products enables optimal usage of the animal carcass, minimizing waste generation in the meat processing industry. Comminuted meat products can be further subdivided into formed ground or emulsion-based meat products based on the degree of comminution that the meat has gone through . ComminuEmulsion-type meat products, such as frankfurters, wieners, and bologna sausage, are made with a viscous mass of finely chopped ground meat that is composed of muscle and fatty tissues. These meat mixtures (batters) are relatively complex systems and have many properties of an emulsion . The disThe overall quality of meat products can be described as a set of properties that together identify what consumers appreciate about a product at purchase and/or consumption. Therefore, the quality traits of meat products are generally those associated with human sensory perception: appearance, including color, shape, and size; taste/flavor; texture; juiciness; and odor. Other than those traits, the quality of meat products is also expressed as freshness or wholesomeness, which relates to the perception that they are safe to eat in terms of being free of pathogens, parasites, toxins, or allergens. The selection of suitable non-meat ingredients for a particular meat application necessitates an understanding of the required functionalities. For example, the quality of emulsion-based meat products is determined by the stability of the emulsion, nutritional composition, and palatability aspects as perceived by the consumers. For burgers or meatballs, the meat particle size is often coarser with less protein extraction desired during mixing, but the need for adequate water and fat-holding is still important to maintain palatability. Thus, high-quality meat products require the application of suitable processing technologies along with meat and non-meat ingredients that offer the required functionalities.The application of non-meat ingredients provides opportunities to enhance the overall quality of processed meat products because of the desirable functionalities they provide to the final product. Generally, food standards of the country regulate the levels of non-meat ingredients in the meat products that are produced or marketed in their jurisdictions. Pulses are dry edible legume seeds consumed in many regions of the world as part of a staple diet and have a long history dating back approximately 11,000 years. In general, pulses are rich in protein, slowly digestible carbohydrates, dietary fiber, and a variety of micronutrients such as selenium, iron, vitamins E and A, folate, niacin, and thiamine . Table 2From a nutritional standpoint, pulses are rich in macronutrients and micronutrients. Thus, pulses in various forms have been utilized in the reformulation of meat products as binders or to enhance their nutritional and healthy features or sensory qualities. The protein, starch, and fiber contents of pulses make them great binders as these biopolymers form complex gel networks with meat proteins . Among tPulses undergo several processes before they are used in food formulations. A summary of pulse processing into various products is shown in The most explicit use of pulses in meat products is as an extender or binder/filler. Extenders and binders serve many purposes in meat products, mostly through their effects on formulation costs, improving nutritional value and benefitting processing parameters such as the viscosity and adhesiveness of meat batters and enhancing product quality by retaining more liquid (oil and water), texture, flavor, mouthfeel, and appearance ,12,13,14The water and oil retention properties of the starch, protein, and fiber constituents of pulse flours lend to their use as a binder in food systems. In products such as patties and burgers, pulse flours trap water, fat, and other substances and form complex gel networks with meat proteins when heated 42). It has been shown that pulse flours enable high liquid retention in meat systems, thereby lowering cooking losses, improving the texture, and increasing the product yield. One particular advantage that pulses have as an ingredient in meat products is their native starches will gelatinize at normal meat thermal processing temperatures , . It has Lean  and fat components are often used in meat product formulations, where protein and fat from skeletal muscles make up the majority of the composition next to water. The lean meat component of the formulation is responsible for fat emulsification, product structure, water binding, and end product color . Plant pStudies have shown that adding pea fiber to meat products increases the cook yield and water retention and reduces shrinkage ,50. ThesBioactive chemicals are often non-nutritive components of food that are present in smaller quantities than macronutrients. Pulses too contain an abundance of bioactive compounds that are not considered nutrients. Phenolic compounds, enzymes, oligosaccharides, and resistant starch are among the bioactive components commonly found in pulses, which makes them suitable for application in a wide range of food products.In a recent comprehensive review, Matallana-González et al.  pointed Among the antioxidant compounds found in pulses, phenolic compounds have a significant role in delaying the oxidative degradation of meat products. Due to their redox characteristics, phenolic compounds are capable of functioning as hydrogen donors, reducing agents, and singlet oxygen quenchers , therebyAlthough bioactive compounds act in beneficial roles, these compounds in raw pulses can also have negative nutritive properties at certain concentration levels ,60,61. TPulses, as with all seeds, contain enzymes such as superoxide dismutase and glutathione reductase, which provide protection against the oxidation of meat . These eConsumers often choose foods based on their sensory appeal including appearance, taste, flavor, and texture. Several studies have shown that pulses are less acceptable because of their natural sensorial properties, especially to those consumers who are not familiar with pulses. In a recent review, Chigwedere and colleagues  identifiColor is another attribute that may be affected when incorporating pulse flours and other pulse-derived ingredients into meat products. The use of some pulse flours in meat products has had a considerable impact on their lightness, redness, and yellowness scores. Dzudie et al.  observedAs shown in In pulse–meat combined product systems, the thermo-incompatibility between pulse and meat proteins is a technical challenge when incorporating pulse proteins with meat . Even thOver the past decade, research has focused on infrared (IR) heating as one of the thermal treatments for pulses to increase their suitability as ingredients for different foods. IR treatment is a rapid heating technique that employs IR electromagnetic radiation. IR heating is considered an advanced thermal process with reported benefits such as environmental friendliness and homogeneity of heating with low energy consumption. Thus, IR heating could serve as a viable treatment that can be used in pulse processing as a pre-treatment to enhance their functional properties.IR heat treatment has been referred to as IR micronizing or micronization, which is a dry thermal process patented by the Micronizing Company UK. The name of the company comes from the unit of measurement of infrared wavelengths . IR is eIR ovens have a modular design that allows them to be easily fitted into most production lines, occupy less floor space than convection ovens, and require less maintenance. IR radiation applied to biological materials is absorbed, reflected, or scattered in the matrix. To maximize the effectiveness of the heating process, the material to be exposed to IR radiation should have a high absorptivity. According to the wavelength range of the incident radiation, energy absorption mechanisms are classified as follows: (1) changes in the electronic state between 0.2 and 0.7 µm (ultraviolet and visible rays), (2) changes in the vibrational state between 2.5 and 1000 µm (FIR), and (3) changes in the rotational state greater than 1000 µm (microwaves) . In geneWhen pulses are heated in an IR oven, the emitted radiation transmits through the air and is absorbed on the outer surface cell layers of the seeds, resulting in an excited, vibrational state of constituent molecules . This geTempering or adjusting seed moisture content (MC) is a common pre-treatment prior to IR heating. In this process step, water or an aqueous solution is applied to raise the MC to a predetermined level. The moisture level, time, and solution composition influence the quality of the end product and would allow the food processor to tailor the pulse ingredient to a specific application. Tempering is an important factor that determines the level of starch gelatinization, protein gelation, and cooking quality in IR heating . AccordiSome studies have shown that a higher seed MC prior to IR heating results in a higher degree of starch gelatinization . An incrThe physicochemical and functional properties of pulse ingredients determine their potential applications in different food products. Nitrogen/protein solubility, WHC, oil absorption capacity (OAC), gelation, and emulsifying capacity (EC) are a few of the functional properties of pulses and pulse-derived ingredients that may be affected by IR heating. Protein solubility is a critical factor that influences its functionality in different food products and can be measured as nitrogen solubility index (NSI) or protein dispersibility index (PDI). IR heating decreased the nitrogen solubility of lentil flour by 33–64% over a pH range of 2–9, which is suggested to be due to heat-induced denaturation of proteins that exposes the hydrophobic regions leading to their aggregation in aqueous solutions . TempereStarch granule gelatinization is the loss of molecular structure when exposed to heating under excess moisture conditions with accompanying viscosity increases and possible gel formation. During gelatinization, water is absorbed into starch granules, resulting in hydration, swelling, and ultimately the loss of birefringence and crystallinity . Due to WHC refers to the ability to absorb, retain, and physically entrap water. It is a critical functional attribute for food applications. Several studies have revealed that tempering followed by IR heating can increase the WHC of flour derived from pulses. According to Bai et al. , tempereOHC is equally as important as WHC when it comes to the utilization of pulse ingredients in foods. Meat products may contain a high percentage of fat that needs to be retained within the product during processing and storage. The type of pulse, seed MC, and IR heating temperature affect the OHC of pulse ingredients. Flours with a higher concentration of hydrophobic amino acids are thought to have a higher oil-holding capacity. Pathiratne  indicatep < 0.01) and negatively correlated with protein solubility . Since IR heating and tempering can both improve starch gelatinization while decreasing protein solubility, they may have opposing effects on the ability of pulse flours to emulsify fat.Emulsifiers provide a cohesive film over dispersed oil droplets in an aqueous medium, preventing structural changes such as coalescence, creaming, flocculation, or sedimentation. This property is important for ingredients added to meat products to stabilize emulsions and prevent finely chopped fat particles or liquid oil (dispersed phase) from coming together. Bai et al.  found thIR heating has been reported to impart minimal negative effects on the nutritional properties of pulses and may contribute to enhanced bioavailability of nutritional components in pulses, primarily through lowering enzyme inhibition and increasing the digestibility ,17,18,94Increased seed temperatures (>100 °C) as a result of IR treatment may have an effect on enzyme activity by inducing conformational changes. Therefore, IR heating has been investigated as a method of inactivating oxidative enzymes, which are associated with the production of off-flavor and off-color in pulses. Shariati-Ievari and colleagues  examinedResults from Shariati-Ievari et al.  and PathPolyunsaturated fatty acids such as omega-3 fatty acids are essential nutrients in the diet and are highly prone to oxidation. In pulses and meat products, VOCs are formed as oxidation products of unsaturated fatty acids. Lipoxygenases in pulses catalyze the oxidation of linoleic acid to generate VOCs. Shariati-Ievari et al.  investigIngredients derived from pulses retain nutritional and functional benefits of the carbohydrates, protein, micronutrients, and phytochemicals of the seed and can be employed in various meat product formulations. Raw/native pulse ingredients have been successfully incorporated as emulsifiers and binders in processed meat products ,117,118.Color is an important consideration when purchasing uncooked burgers or fresh sausage and can be assessed using instrumental methods and visual observation. According to Fernández-Lopez et al. , the redOne of the most unique functionality benefits of incorporating lentil flour from IR-heated lentils was the improvement of color stability of fresh beef products during storage. Der  evaluateOne study set out to define the optimum IR heating conditions of green lentils for maintaining meat product redness . HeatingThe pulse type, market class, variety, and quantity applied of IR-treated pulse ingredients used in meat products may also influence color stability. However, according to Der , both thMore recently, focus shifted to investigating which part of the lentil seed could be attributed to color stability in meat products. Li  investigMyoglobin (Mb) is the main meat pigment that determines the red color of meat. Therefore, maintaining the brown-colored metmyoglobin (metMB), the oxidized form of Mb, at low levels is vital for meat products. There was consensus that the delay in the oxidation of Mb and the stability of the red color in products formulated with heat-treated lentils was related to the inactivation of oxidative enzymes (such as lipoxygenase) and the concomitant increase in antioxidant activity ,17,18. AFrozen meat products also benefit from formulating with pulse ingredients. An assessment of lentil ingredients in frozen beef burgers  confirmeSimilar to the results observed for refrigerated burgers, IR-treated lentil flour exhibited stronger antioxidant effects in beef burgers than toasted wheat crumb (6%), due to the endogenous antioxidants, phenolic compounds, and enzymes. Overall, work by Li  suggesteOxidation of unsaturated lipids is a major contributor to the loss of quality in both fresh and processed meat, which continues during refrigerated or frozen storage of the products. The quality of oxidized meat degrades in different ways, including changes in color, flavor, texture, and nutritional value. There is clear evidence that pulse ingredients that have been subjected to IR radiation minimize lipid oxidation in both fresh and frozen meat ,18. In l5 units/mL to 1.3 × 105 units/mL [5 units/g protein to 40.5 × 105 and 0.4 × 105 units per g at heating temperatures of 115 °C and 130 °C, respectively, and there was no significant difference in lipoxygenase activity when the lentil was heated above 130 °C [Optimum reductions in the lipid oxidation of beef patties with the addition of IR-heated lentil flour appear to be related to the use of higher-temperature IR heating conditions. IR treatment at 130 °C and 150 °C was more effective than 115 °C in delaying oxidation, and this was attributed to the lower (or lack of) residual activity of the oxidative enzymes . It has units/mL . With ree 130 °C . Additioe 130 °C  rather tThe antioxidant properties of the different types of lentil ingredients were found to be at their highest in whole lentil flour and hull fiber derived from heat-treated seeds, which is in agreement with effects previously described for color retention . Low-fatThe susceptibility of meat products to lipid oxidation is influenced by a variety of variables, including product type, meat species, fat composition, and other additives. Even though there have been few research studies conducted on various meat products, the available evidence indicates that meat products benefit from the application of IR pre-treatment to pulses when they are utilized as an ingredient in the formulation. Burgers are formed meat products made of ground meat mixed with other ingredients and additives, and they may contain varying amounts of fat. In low-fat (<10% fat) beef burgers, IR-treated lentil ingredients have increased the stability of the unsaturated lipids ,17,18. WThus far, most studies on the addition of IR-heated lentil flour focused on low-fat meat products. Recently, Kim and Shand  studied The oxidation of myoglobin and unsaturated lipids in muscle-based foods have a close relationship, and some studies show inter-dependencies . The stuEmulsion-type meat products  as a replacement for phosphates (sodium tripolyphosphate: STTP) in uncured bologna-type chicken sausage, confirmed that lentil flour (6%) was effective in delivering the liquid-binding properties similar to synthetic phosphates.Alkaline phosphates are commonly used to improve the water retention and textural properties of cooked meat products by increasing the pH and ionic strength ,128,129.Texture is a multi-parameter sensory property that is linked to the product’s structure, mechanical, and surface properties . ProteinThe type/market class and quantity of the pulse used, as well as the type of product, will determine the overall effect on their texture. The evidence available to date, however, shows that the type of pulse  or the level of the addition of IR-heated pulse flours has little impact on the texture properties beyond that of the untreated flour control. For example, the application of IR-treated lentil flour at 6% to 12% in burgers  produced®, 43% starch and 25% protein), despite the fact that the product was IR treated [The use of pulse ingredients influences the sensorial quality of meat products ,133,134  treated . It was  treated  and toas treated .The appearance/color of meat products is another sensory property that consumers critically consider. Pulses, especially those with colored seed coats, contribute to the product color and may be difficult to mask in meat products. In chicken bologna, the addition of IR-treated lentil flour and seed coat a.This effect was also reflected by the redness (a*) values; a lowering of the red color was observed when lentil components were added to bologna, and a higher redness was observed in the control sample. Pulse ingredients can bring a grainy appearance when seed coat component-rich material is used as observed by Xu  with porToday’s food industry is reengineering processed food formulations that can deliver sufficient dietary proteins, consisting of both animal and protein sources. Blending meat and plant-based ingredients is an ideal approach to reduce meat content and incorporate plant proteins and other nutrients, as well as bioactive phytochemicals, into products without compromising the overall nutritional and sensory quality. In general, pulses, including lentils can be blended with meat and bring comparable nutritional profile to that of animal muscles. Lentils, in particular, can be processed to yield several ingredients differing in nutrient/chemical composition and techno-functional characteristics, as illustrated in The enhancement of the antioxidant properties of lentil ingredients was one of the most distinctive functional benefits of IR heating of lentils. This particular improvement resulted in extending the stability of the color and delaying lipid oxidation in both raw and cooked meat products, in either refrigerated or frozen conditions. An increase in oxidative stability in combination with the red color retention (less brown color) of meat products reduces the rejection of meat products at the grocery aisle due to low consumer appeal. The incorporation of lentil flour into meat products increased the liquid-binding capacity and thus the product cook yield as sufficiently as its untreated counterpart. The product cook yield is a significant quality consideration of the cooked, ready-to-eat product offerings at home and at food services. The effect of IR treatment on the textural properties and nutritional composition was minimal, having no detrimental effects compared with the commercial binders used in meat products. IR heat-treated lentil flour is a natural ingredient possessing antioxidant properties that can be used in developing clean-label products. The limitations of lentil flour, especially whole flour, could be the contribution to foreign flavor and, at high addition levels, the masking of the reddish color of the products. Although IR pre-treatment did not show a significant control to these limitations, these effects depend on the level of addition to the product, necessitating studies to optimize the level of addition in meat products without compromising product flavor and taste.Overall, adding IR-treated lentil ingredients promotes meat product yields, oxidative stability, nutritional availability, and sustainability measures associated with animal foods. In the development of lentil-enriched meat products, the use of IR heat treatment widens the applications beyond the primary functions of binders. Introducing this ingredient into plant–meat blended products could bring viability and improve the sustainability of protein-rich food products.The macro-components of pulses provide the necessary binding ability of meat particles in comminuted meat products, stabilization of heat-treated meat-fat emulsions, and absorbent function of expressed liquid from meat particles during heat treatment. Heat pre-treatments, particularly those that use IR heat, have been effective in modifying macromolecules and minor components of pulses, as evident from the research carried out with lentils. The changes brought about by IR pre-treatments have been beneficial to meat products. The prolongation of a fresh red color, lowering of lipid oxidation products, and flavor changes were the most prominent benefits of IR pre-treatment, while the texture-forming properties were generally not affected significantly from those observed with the addition of raw, unheated pulse ingredients. IR-treated pulse ingredients, especially lentil-based, may also be used to replace commercial binders such as toasted wheat crumbs, isolated soy protein, and corn starch, as well as synthetic antioxidant compounds, in the transition to clean-label products. Thus, in the development of pulse-enriched meat products, the use of IR heat treatment further widens the applications beyond the primary functions of binders and will be a viable approach toward improving the sustainability of protein-rich food products. The effects of IR heat treatment on the properties of pulses and how they perform in meat products, however, were mostly limited to lentils and chickpeas, especially with whole and dehulled flours in a limited number of meat products. Studies carried out so far show the effects of IR treatment vary depending on the chemical composition of the seed components, seed coats and cotyledons, and thus how they affect the components of meat in product systems. Future research needs to focus on assessing the effects of IR heating on different pulses and their derived ingredients and their impact on a wide range of meat products. The efforts to date have been successful through the cooperation of scientists across a number of disciplines. Expanding on the multi-functional benefits of pulses requires an intimate knowledge of the ingredient functionalities and requirements of the specific meat system.


Have neonatal intensive care resources at hospitals where infants born extremely preterm are delivered changed over the past decade?In this cohort study including 357 181 infants born at 22 to 29 weeks’ gestation between 2009 and 2020, births at neonatal intensive care units (NICUs) with lower levels of care or lower birth volumes increased, while births at NICUs with higher levels of care or higher birth volumes decreased.The findings of this study suggest increasing deregionalization of extremely preterm birth. This cohort study examines the distribution of extremely preterm births between 2009 and 2020 stratified by neonatal intensive care resources at the delivery hospital in the US. In an ideal regionalized system, all infants born very preterm would be delivered at a large tertiary hospital capable of providing all necessary care.To examine whether the distribution of extremely preterm births changed between 2009 and 2020 based on neonatal intensive care resources at the delivery hospital.This retrospective cohort study was conducted at 822 Vermont Oxford Network (VON) centers in the US between 2009 and 2020. Participants included infants born at 22 to 29 weeks’ gestation, delivered at or transferred to centers participating in the VON. Data were analyzed from February to December 2022.Hospital of birth at 22 to 29 weeks’ gestation.Birthplace neonatal intensive care unit (NICU) level was classified as A, restriction on assisted ventilation or no surgery; B, major surgery; or C, cardiac surgery requiring bypass. Level B centers were further divided into low-volume (<50 inborn infants at 22 to 29 weeks’ gestation per year) and high-volume (≥50 inborn infants at 22 to 29 weeks’ gestation per year) centers. High-volume level B and level C centers were combined, resulting in 3 distinct NICU categories: level A, low-volume B, and high-volume B and C NICUs. The main outcome was the change in the percentage of births at hospitals with level A, low-volume B, and high-volume B or C NICUs overall and by US Census region.A total of 357 181 infants  were included in the analysis. Across regions, the Pacific (20 239 births [38.3%]) had the lowest while the South Atlantic (48 348 births [62.7%]) had the highest percentage of births at a hospital with a high-volume B– or C-level NICU. Births at hospitals with A-level NICUs increased by 5.6% , and births at low-volume B–level NICUs increased by 3.6% , while births at hospitals with high-volume B– or C-level NICUs decreased by 9.2% . By 2020, less than half of the births for infants at 22 to 29 weeks’ gestation occurred at hospitals with high-volume B– or C-level NICUs. Most US Census regions followed the nationwide trends; for example, births at hospitals with high-volume B– or C-level NICUs decreased by 10.9% [95% CI, −14.0% to −7.8%) in the East North Central region and by 21.1%  in the West South Central region.This retrospective cohort study identified concerning deregionalization trends in birthplace hospital level of care for infants born at 22 to 29 weeks’ gestation. These findings should serve to encourage policy makers to identify and enforce strategies to ensure that infants at the highest risk of adverse outcomes are born at the hospitals where they have the best chances to attain optimal outcomes. We examined regionalization trends in the birthplace NICU level, incorporating NICU volume from 2009 to 2020 overall and by US region among newborns born at 22 to 29 weeks’ gestation.STROBE) reporting guideline.For this cohort study, the use of VON’s deidentified research repository was determined to be exempt from review and informed consent by the University of Vermont’s committee for human research because it was not human participants research. This study is reported following the Strengthening the Reporting of Observational Studies in Epidemiology  and high-volume . Since many studies have also found no outcome differences between high-volume B–level vs C-level centers ,25 we combined high-volume B–level and C-level centers,25 resulting in 3 distinct NICU categories.For VON members, NICU level is collected through the annual membership survey and classified as level A, restriction on assisted ventilation or no surgery; B, major surgery; or C, cardiac surgery requiring bypass. Given the strong association between the preterm infant admission volume and mortality,26 Member NICUs contribute data from medical records using standardized VON forms. Maternal race and ethnicity were obtained by personal interview with the mother, review of the birth certificate, or medical record, in that order. Race and ethnicity were classified as American Indian, Asian, Black, Hispanic, White, and other . Race and ethnicity were included in analysis as descriptive characteristics.In January 2015, VON started collecting the names of the non-VON birth center for outborn infants transferring to a VON center. For these non-VON centers, we obtained the center’s level of neonatal care from publicly available resources. Level I centers were coded as having a well-baby nursery; level II, having an equivalent of VON’s classification of level A NICU; level III, having a level B NICU; and level IV, having a level C NICU. Centers with well-baby nursery and those with level A NICUs were combined into 1 group, labeled as level A NICUs. For outborn infants born before 2015, we did not know the NICU level of the non-VON center, so we only included infants transferred to VON centers within 3 days of life because we were more confident in assuming that these infants were being transferred from hospitals with level A NICUs or well-baby nurseries. NICU census regions and divisions were classified according to the US Census Bureau classifications as West (Mountain and Pacific), Midwest , South , and Northeast (New England and Mid-Atlantic)  to estimate the probability of birth in each NICU level, along with 95% CIs. We also calculated the absolute change in the percentage of births along with the 95% CI by NICU level between 2009 and 2020.We conducted sensitivity analyses to determine the robustness of our findings. The first analysis was conducted restricting to hospitals that were in the sample the whole period. The second analysis recoded the missing NICU level into low-volume B instead of A for outborn infants transferred to VON centers within 3 days of life. A third analysis used a complete case analysis without recoding the missing level for outborn infants transferred within 3 days of life.P values were 2-sided, and statistical significance was set at P = .05. Statistical analyses were conducted using R software version 4.0.5 . Data were analyzed from February to December 2022.From 2009 to 2020, 322 407 neonates were inborn at 822 VON centers and 39 652 were outborn and transferred to 714 VON centers. Among the inborn infants, 305 866 infants (94.9%) did not die in the delivery room and were admitted to the NICU. Among the outborn infants, 17 464 infants transferred from a birth center with a known NICU level. Among the remaining 22 188 outborn infants, 20 087 (90.5%) were transferred within 3 days of life  and were classified as transferred from a hospital with a well-baby nursery or level A NICU, and 2100 (9.5%) were transferred after 3 days of life and were classified as missing birth NICU level  were born at centers with restricted ventilation. Among infants born at 22 to 29 weeks’ gestation, most regions followed the nationwide trends  among infants born at 32 weeks’ gestation or less.13 This association was even stronger among infants weighing less than 1000 g .13 Another study showed that among newborns younger than 28 weeks’ gestation in a tertiary hospital, birth in a nontertiary hospital was associated with increased odds of mortality , while transfer from a nontertiary to a tertiary hospital in the first 48 hours was associated with increased odds of severe brain injury.27 In addition to the large number of studies on the benefits associated with being born at a hospital with a high-level NICU, an evolving body of evidence emphasizes the importance of NICU volume.25 Collectively, these studies provide strong evidence that the best outcomes are associated with delivery at high-level and high-volume NICUs.27Previous research has shown the importance of birth at a hospital with a high-level NICU for infants who are very preterm or have VLBW.23 For example, a study by Wehby et al23 found that being delivered at hospitals with low-volume NICUs (<50 infants with VLBW per year), compared with high-volume NICUs (>100 infants with VLBW per year), was associated with nearly 2-fold  increased mortality odds under the classic risk-adjusted model, compared with more than 5-fold  increased mortality odds using the instrumental variable approach.23However, previous studies have underestimated the benefits associated with regionalization and with NICU level and NICU volume. Given that patients who are more severely ill are more likely to deliver at hospitals with high-level and high-volume NICUs, studies conducted using instrumental variables methods that control for this unobserved selection bias show even stronger associations with improved neonatal outcomes when infants with high risk of adverse outcomes are delivered at high-level and high-volume NICUs, compared with low-level and low-volume NICUs.6 Almost all (88%) of the VLBW deliveries at these new NICUs were shifted from hospitals with a high-volume B– or C-level NICU.6 Thus, this increase in midlevel NICUs was essentially all deregionalization, not improved NICU care access.6We speculate that the increasing share of infants delivered in hospitals with level A or low-volume B–level NICUs is accounted for by a decrease in the share of infants delivered in hospitals with high-volume B– or C-level NICUs. A study conducted in California examined how delivery location shifted in response to midlevel NICU openings .7 In California, for example, approximately 80% of births that occurred in smaller, lower-level NICUs were located within 25 miles of a large tertiary NICU.12 This deregionalization in perinatal care has occurred due to the wide availability of neonatal intensive care technologies and neonatologists, economic factors and financial incentives derived from installing new NICUs,28 and different state policies that influence regulation of regionalized systems.29 The effects of these different factors have also varied by US region, contributing to the regionalization trends we observed.The proliferation of midlevel NICUs is mainly happening in urban and suburban areas that are already being served by tertiary centers.Between 2009 and 2020, births at high-volume B– or C-level NICUs decreased between 3.7% and 21.2% and births at level A NICUs increased between 4.2% and 14.0%. Births at low-volume B–level NICUs increased across 6 regions, and the increase ranged between 2.3% and 8.1%. Only 2 regions stood out in their regionalization trends: the East South Central and Pacific regions. The East South Central region had a lower regionalization rate at the start of the study and shifted toward the national mean by the end, while the Pacific region had a minor change in the delivery site during the study period because this region had already experienced a decline in the level of NICU care regionalization.30 in the Pacific region occur in California. California had already shown deregionalization trends starting in the 1990s.31 This trend continued and between 1990 and 2001: there were 48 new midlevel units established or upgraded,6 and between 2005 and 2011, the overall percentage of infants with VLBW born at hospitals providing the highest degree of care  decreased from 42.5% to 26.5%.19Most births at 22 to 29 weeks’ gestation (75%)32 This resulted in more than 90% of all very preterm deliveries occurring in a hospital with a high-volume NICU, which was associated with decreasing the neonatal mortality rate from 8.1 deaths to 2.7 deaths per 1000 live births.32 On a local level, in the greater Cincinnati, Ohio, region, the implementation of perinatal outreach programs stressing the importance of transfer of mothers with high risk to subspecialty perinatal centers decreased the percentage of infants with VLBW delivered at hospitals without tertiary perinatal care from 25% to 11.8%.33Given the negative outcomes associated with birth at nontertiary or low-volume hospitals, the fact that more than 50% of these infants with high risk were born in hospitals with level A and low-volume B–level NICUs is unacceptable. Previous studies have shown that the delivery of these infants with high risk can be shifted to tertiary centers if there is a systemwide effort. In 1990, Portugal closed all the small maternity units and small NICUs and implemented a system to facilitate maternal transport of high-risk deliveries.34This study has several limitations. One limitation is the missing data on the birth hospital level for a large proportion of outborn infants. We analyzed our data by region, which might mask individual state-level differences. To classify the NICU level for outborn infants, we used publicly available resources, which might have incomplete and inaccurate web descriptions that claim a higher level of care.In this cohort study of neonates born at 22 to 29 weeks’ gestation, we identified concerning deregionalization trends. Given the strong evidence showing risk of worse outcomes when the birth of newborns with high risk does not occur at large tertiary centers, our findings should serve to encourage policy makers to identify and enforce strategies that ensure that infants with the highest risk of adverse outcomes are born at the hospitals where they have the best chance to attain optimal outcomes.


The aim of the study was to develop a computerized method for distinguishing COVID-19-affected cases from cases of pneumonia. This task continues to be a real challenge in the practice of diagnosing COVID-19 disease. In the study, a new approach was proposed, using a comprehensive set of diagnostic information (CSDI) including, among other things, medical history, demographic data, signs and symptoms of the disease, and laboratory results. These data have the advantage of being much more reliable compared with data based on a single source of information, such as radiological imaging. On this basis, a comprehensive process of building predictive models was carried out, including such steps as data preprocessing, feature selection, training, and evaluation of classification models. During the study, 9 different methods for feature selection were used, while the grid search method and 12 popular classification algorithms were employed to build classification models. The most effective model achieved a classification accuracy (ACC) of 85%, a sensitivity (TPR) equal to 83%, and a specificity (TNR) of 88%. The model was built using the random forest method with 15 features selected using the recursive feature elimination selection method. The results provide an opportunity to build a computer system to assist the physician in the diagnosis of the COVID-19 disease. In 2019, the coronavirus disease, known as COVID-19, began to propagate, swiftly evolving into a global predicament of considerable magnitude. The COVID-19 outbreak precipitated an unforeseen and distressing scenario, ushering the entire world into the throes of a pandemic and resulting in the tragic loss of thousands of lives. According to the World Health Organization (WHO), COVID-19 has extended its reach to 220 countries and territories, with recorded infections and fatalities exceeding 771 million and around 7 million, respectively, as of 26 September 2023 . This deThe infection caused by severe acute respiratory syndrome coronavirus (SARS-CoV-2) leads to a wide range of clinical manifestations, from completely asymptomatic to severe acute respiratory distress syndrome, which complicates the diagnostic process ,8. The mGenerally, the diagnosing of COVID-19 can be achieved using three different methodologies: real-time reverse transcriptase-polymerase chain reaction (RT-PCR), chest CT imaging scan, and numerical laboratory tests [RT-PCR tests are fairly quick, sensitive, and reliable. The sample is collected from a person’s throat or nose; some chemicals are added to remove any proteins, fats, and other molecules, leaving behind only the existing ribonucleic acid (RNA). The separated RNA is a mixture of a person’s RNA and the coronavirus RNA, if present. The RT-PCR test suffers from the risk of false negative and false positive results. Consequently, the spread of COVID-19 infection has increased because RT-PCR tests cannot immediately distinguish the infected people. Although several studies have observed that the sensitivity of chest CT in the diagnosing of COVID-19 is higher than that of RT-PCR, CTs and X-rays are not accurate tools for diagnosing COVID-19 ,11. TherRecently, machine learning is an adjunct tool for clinicians. Machine learning can automatically support medical diagnosis as a helping tool for identifying and detecting the novel coronavirus ,14,15. HThe Ghaderzadeh M. et al. review study presents a comprehensive assessment of the current status of all models utilized in the identification and diagnosis of COVID-19 using radiology techniques, particularly those relying on deep learning for processing . The resIn the article by Ali R.H. et al., a model for predicting the COVID-19 virus was introduced leveraging feature selection techniques [In the article by Shanbehzadeh M. et al., the dataset comprises 501 case records categorized into two classes: COVID-19 and non-COVID-19. It encompasses 32 columns, each representing diagnostic features . VariousThe study described by Huang C. et al. described a diagnostic strategy for COVID-19 known as feature correlated naive Bayes (FCNB) . FCNB enIn the work of Ahamad, Md.M. et al., a model was developed using supervised machine learning algorithms with the aim of identifying the key features that could serve as predictive markers for diagnosing COVID-19 . The feaThe study of Alotaibi A. et al. focused on the early prediction of disease severity using patient history and laboratory data, employing three distinct types of artificial neural networks (ANNs), support vector machines, and random forest regression with various learning methods . FeatureAs presented in article Chauhan H. et al., an artificial neural network served as the diagnostic model for assessing coronavirus positivity . The resIn the article by Srinivasulu A. et al., a function for predicting diseases caused by the COVID-19 virus was introduced . This reThe aim of the study described by Ghaderzadeh M. et al. was to develop an efficient computer-aided detection (CAD) system for the COVID-19 virus using a neural search architecture network (NASNet) . A localIn the study presented by Pourhomayoun M. et al., a dataset of over 2,670,000 laboratory-confirmed COVID-19 cases from 146 countries was used, including 307,382 labeled samples . To addrNew studies are constantly appearing in the literature, presenting various methods of diagnosing COVID-19 and distinguishing it from other diseases that cause changes in the lungs, such as pneumonia. These methods use different parameters from the study of patients. The most popular trend in the diagnosis of COVID-19 is the analysis of X-ray images ,28,29,30Many methods have been provided for COVID-19 detection based on machine learning techniques. Despite the efficiency of these methods, they suffer from many limitations, such as low diagnosis accuracy, high complexity, and long prediction time. The work in this paper is concentrated on providing a new COVID-19 diagnosis system based on NLTs, which have proven to be the most effective methodology for COVID-19 diagnosis. The development of a disease prediction model based on clinical variables and standard clinical laboratory tests is proposed. The clinical variables include demographics, signs and symptoms, laboratory test results, and COVID-19 diagnosis. The variables include age, sex, serum levels of neutrophils, serum levels of leukocytes, serum levels of lymphocytes, reported symptoms , body temperature, and underlying risk factors .The new contribution and value of the conducted study is that the authors have attempted to distinguish the cases of COVID-19 infection from the cases of pneumonia. This is a far more difficult task than distinguishing COVID-19 virus infection from completely healthy cases. The latter task is often described in the literature, while from the point of view of complexity, it is much easier than the one presented. Furthermore, the proposed method of diagnosis is based on a comprehensive set of diagnostic information using multiple sources of information. This approach is more reliable compared with the proposed solutions based on a single source of information such as radiological imaging.The COVID-19 dataset is a real dataset that is used to detect COVID-19 patients. These raw hospital data contain the results of clinical laboratory tests and clinical variables collected from different cases who were admitted to the hospital . The clinical variables include demographics, symptoms, laboratory test results, and COVID-19 diagnosis. The variables include blood test results (various blood properties), reported symptoms , body temperature, anamnesis data, and underlying risk factors . The total number of cases in this real dataset is 128. According to this dataset, it is considered two class categories, called COVID cases and non-COVID cases, as shown in The distribution of the cases used in the collected dataset is represented according to age and gender, as shown in The important features of basic information, symptoms , diagnostic results, prior disease, symptom history  that are directly or indirectly related to the COVID-19 disease were extracted. The patients did not all develop the same symptoms; symptoms of every patient were found individually. In detail, some keywords for each feature were selected, and then those keywords were matched with text data and the features were extracted individually. However, much of the data was in the form of unstructured text information, which can be difficult to process. The data contained patient symptoms in a text format. Categorical data were converted to dummy variables, because non-numerical data are not allowed in the adopted machine learning algorithm. A data transforming process was applied through transforming the data into numeric forms (“0” and “1”). The true value (“1”) means the existence of the feature on a patient, while the false value (“0”) means the absence of the feature. Lastly, a final dataset, which contained the main features, was generated. In this dataset, most of the values were binary, and several fields were numerical values. Brief descriptions of some features that affect COVID-19 patients are shown in Before the essential steps of machine learning began, the data were preprocessed . This stHandling categorical data. The original data contained a number of categorical features with text labels. In contrast, most machine learning algorithms require numeric feature values to work properly. Therefore, the categorical data were encoded with dummy variables. After this operation, the value of each categorical feature was “1” if there was a symptom associated with the feature, or “0” otherwise.Data imputation. The initial dataset contained missing values (about 10%). Such a situation can cause errors during data analysis. To avoid this, various methods of data imputation are used. One of the most popular methods, referred to as replace with the summary, was applied. Missing categorical values were replaced with the value occurring most often (mode), while continuous values were replaced with the average value.Data resampling. Initially, the dataset consisted of 128 cases. Its structure was such that there were 77 cases (60%) diagnosed with the COVID-19 disease and 51 cases (40%) with diagnosed pneumonia (non-COVID-19). The dataset was unbalanced, which could risk biasing the prediction of the majority class by classifiers built on its basis. To avoid this danger and ensure a balanced number of positive and negative observations, a resampling method known as a synthetic minority oversampling technique (SMOTE) was used. The SMOTE technique is based on the fact that the size of the minority class is increased by generating new synthetic cases by combining all K-nearest neighbors  of the minority class using similarities in feature space (Euclidean distance). After applying the SMOTE technique, the dataset counted 154 cases, with both classes (COVID-19 and non-COVID-19) having 77 cases each.Dataset splitting (train/test). The full dataset (154 observations) was randomly divided into a training and a testing part in such a way that the training part accounted for 70% and the testing part for 30% of the full set. During the division, the same proportion (50%) between observations belonging to both classes was maintained as in the full set. Details of the size of each subset are provided in Data rescaling. Ninety-nine feature descriptors were obtained as a result of assessing the patients’ diagnosis parameters. Due to the fact that the feature descriptors were measured on different scales , the features were rescaled using classical standardization: ijx is the value of feature j for observation i; ijz is the standardized value of feature j for observation i; j; and js is the standard deviation of feature j. After standardization, the interval scale and normal distribution N were in effect for all features. Scaling was performed once for the training data. Then, the test data were transformed in a similar manner. During this process, the mean and variance vectors obtained during the standardization of the training data were used for each feature.Data cleaning. A four-step data cleaning procedure was carried out:Removal of the features taking constant values ;Removal of the features assuming almost constant values (variance less than 0.01);Removal of the features assuming duplicate values;r) was used, which detects linear dependencies between features and assumes normality of their distribution. The features for which |r|> 0.33, indicating the presence of a moderate and strong correlation, were removed.Removal of the mutually correlated features. The Pearson correlation coefficient  set of features to a certain subset that includes the features that are important for the criterion used. As a result of data cleaning, the full set of 99 features was reduced to 23 features. Such a significant reduction was mainly due to the presence of a large number of features with moderate and strong cross-correlation. Therefore, at the selection stage, an additional reduction was abandoned, and the goal was to build a ranking list for each of the selection methods used. The ranking lists contained a set of features in the order that reflects their importance in the process of identifying the observations related to one or another diagnosis. As a result of this processing step, nine training sets and nine test sets were obtained.Univariate Fisher’s method (FISHER) and the method of analysis of variance (ANOVA);Multivariate Relief method (RELIEF).Filter methods:Sequential forward selection (SFS);Sequential backward selection (SBS);Recursive feature elimination along with LogisticRegression estimator (RFE).Wrapper methods:SelectFromModel method with logistic regression (LR) evaluation;SelectFromModel method with AdaBoost (ADA) evaluation;SelectFromModel method with LightGBM (LGBM) evaluation.Embedded methods:Nine feature selection methods were used. Their acronyms are given in brackets.X be the general set of objects and Y be the set of possible labels of objects connected by some unknown dependency y: X → Y. The task of supervised learning is to construct an algorithm C that approximates the y dependence on a known training sample T = {, …, } according to some measure of quality Q. In the case of a classification problem, each object is described by some discrete variable that the algorithm tries to predict; that is, to predict the class of the object. The simplest classification task is binary classification, in which there are two classes of objects.The goal set in the research was to solve a binary classification task, which belongs to supervised learning problems. Let us give a general formulation of the problem of learning with a teacher. Let During the study, the grid search method was used in the training process, which made it possible to determine the optimal values of the hyperparameters of each model. The GridSearchCV method, available in the model_selection module of the scikit-learn library, was used for this purpose. The models were evaluated based on 10-fold cross-validation. Each model, which was considered optimal in terms of a given number of features, was saved as a file on a disk. Then, the best model was selected from the available set . Such a model is named as optimal in Linear discriminant analysis (LDA);Logistic regression (LR);Support vector machines with the C regularization parameter (SVM);Support vector machines with the nu parameter to control the number of support vectors (NuSVM);K-nearest neighbors (KNN);Decision trees (DT);Multilayer perceptron (MLP).Methods to build a single model:Random forest (RF);Gradient boosting (GRADBoost);AdaBoost model combination (ADABoost);eXtreme gradient boosting (XGBoost);Light gradient boosting machine (LGBM).Ensemble methods:Twelve different supervised learning methods were used to build the classifiers. Their acronyms are given in brackets.The basis for calculating the value of model quality metrics was the confusion matrix , the eleThe evaluation of the models using the test set resulted in the calculation of overall classification accuracy (ACC), sensitivity , specificity (TNR), precision (PREC), F1 index (F1), and area under the ROC curve (AUC). The number of features in the lists built by the separate methods was as follows: filter and wrapper methods—23; embedded methods: logistic regression—8, AdaBoost—6, LGBM—23 . The selAs In the process of training the classification models, nine training sets (one for each selection method) were used, arranged in accordance with the ranking of features. It should be emphasized that in this scenario of the study, the full set of features was used; i.e., the one that was returned by a given selection method. While the models were being trained, the number of features in the training set changed according to the ranking list. For the filter and wrapper methods, this number varied from 2 to 23; for logistic regression, from 2 to 8; for the AdaBoost method, from 2 to 6; and for the LGBM method, from 2 to 23.On the basis of the results presented in The criterion for selecting the best model was the same as for selecting the optimal model; i.e., the highest validation accuracy with the minimum number of features in the training set. For the RF model, the validation accuracy was 0.92, and 21 features were used. For all filter and wrapper methods, a model selection procedure was used that was previously characterized using the Fisher method. In the case of embedded methods, only those classifiers that were used in the selection process were constructed and configured.The summary results are presented in When several learning algorithms were used, many different models were evaluated. From these, the most effective model was selected, which could then be used to forecast future new data. It should be added here that the parameters of the procedures used for data preprocessing, such as feature scaling or dimensional reduction, were determined solely on the basis of the training set. Then, they were used to transform test data and new data. The following models had the highest classification accuracy for the test sample: F-RF-21 (ACC = 85%), R-RF-23 (ACC = 85%), SBS-RF-22 (ACC = 85%), and RFE-RF-15 (ACC = 85%). The accuracy of testing other models was not much worse, and fluctuated between 81% and 83%. The results of the models’ validation are listed in The classification accuracy of all the models for the test set exceeded the level of 80%. It should also be noted that almost all models achieved a higher TNR than TPR. The high quality of the four models mentioned above is also confirmed by the ROC curves in The RFE-RF-15 model was created using 15 features. In Among the advantages of the conducted study is that, unlike many studies presented in the literature, the obtained results are not on distinguishing the cases of COVID-19 disease infection from healthy cases, which seems like an easy task, but on distinguishing the cases of COVID-19 infection from cases of pneumonia. This task is definitely more difficult and remains a real challenge in the practice of COVID-19 disease diagnosis. The information obtained through medical history, demographic data, signs and symptoms of the disease, as well as laboratory test results was used. These data form a comprehensive set of diagnostic information that is far more reliable than any other data, such as those based only on radiological imaging. In contrast, some drawbacks of the conducted study include the relatively small number of observations used (128) and the unbalanced original dataset, which necessitated the use of the resampling technique.Comparing the obtained results with studies published in the literature, significant differences related to the material used in the research can be seen. One of the best results presented in the literature is the combination of chest x-ray and CT images proposed in . The utiStudies published present a classification method that leverages radiomics features extracted from CT chest images to distinguish the patients with COVID-19 from those with other forms of pneumonia . For theThe study by Zhao B. et al. tested the usefulness of vibrational spectroscopy combined with machine learning for early screening of COVID-19 patients . The autAnother avenue in COVID-19 diagnostics involves laboratory tests, which have garnered substantial validation in numerous studies for their significant diagnostic efficacy. Routine blood tests present a cost-effective and rapid means of COVID-19 detection. These precise algorithms can prove highly effective, especially during the peak of a pandemic when hospitals are inundated with patients. The research presented by Chadag K. et al. shows that the use of machine learning methods allows achieving satisfactory diagnostic results . In thisA novel diagnostic model for COVID-19 has been developed that significantly improves the overall prediction accuracy . The datWhen analyzing the studies presented in the literature, it can be seen that the use of clinical and laboratory data has great diagnostic potential. The number of publications proves the intensive search for the algorithms that give better and better results and the importance of this diagnostic method. The results presented in this article do not differ in effectiveness from others presented in the literature. This proves the potential of the method used. This method can become an alternative to expensive imaging tests .The main result of the study corresponds to the feature vectors obtained on the basis of numerical clinical laboratory tests, which, together with the proposed classification methods, can be an element of a computer system that supports the doctor in the diagnostic process. This system will allow the classification of analyzed cases into patients who need to detect cases of COVID-19. Nine of the constructed models achieved classification accuracy at a level exceeding 80%. On the basis of the results obtained, two models were adopted as a proposal for the final solution of the problem of automatic data classification, supporting the doctor in the diagnosis of the infectious disease COVID-19. They are RFE-RF-15 and E-ADA-4, since these models had higher accuracy than other models and worked with fewer features. The first is a classifier built using the random forest. This model uses 15 features selected using the recursive feature elimination along with logistic regression estimator (RFE) method. For the data belonging to the test set, this resulted in accuracy of 0.85 (85%), sensitivity of 0.83, and specificity of 0.88. The second model was built using the SelectFromModel method with AdaBoost classifier, which resulted in accuracy of 0.83 (83%), sensitivity of 0.83, and specificity of 0.83. This model uses four features.nu parameter to control the number of support vectors (NuSVM), K-nearest neighbors (KNN), decision trees (DT), and multilayer perceptron (MLP). In addition, five ensemble methods were used: random forests (RF), gradient boosting (GRADBoost), AdaBoost model combination (ADABoost), eXtreme gradient boosting (XGBoost), and light gradient boosting machine (LGBM).The study used three filter methods (univariate-Fisher’s method (FISHER), the method of analysis of variance (ANOVA), and multivariate-relief method (RELIEF)), three wrapper methods , sequential backward selection (SBS), and recursive feature elimination along with LogisticRegression estimator (RFE)), and three embedded methods (SelectFromModel method with logistic regression (LR) evaluation, SelectFromModel method with AdaBoost (ADA) evaluation, and SelectFromModel method with LightGBM (LGBM) evaluation). Each method of feature selection selected a different number of features. Twenty-three features were selected by the filter and wrapper methods; with the embedded methods the results were as follows: logistic regression—8 features, AdaBoost—6 features, and LGBM—23 features. The features revealed by the filter and wrapper methods were used by 12 supervised machine learning methods. These were: linear discriminant analysis (LDA), logistic regression (LR), support vector machines (SVM), support vector machines with the The plan for further work in the scope presented in the dissertation includes attempts to create a complete system for diagnosing infectious COVID-19 diseases based on clinical and laboratory data. First of all, the developed algorithm needs to be tested on a larger number of patients. This is one of the most problematic issues when looking for new solutions in medical diagnostics. Difficulties in accessing relevant data-protected patient databases and finding results that meet strict criteria usually force investigators to conduct studies on fewer samples. When building systems that aim to create solutions commonly used in healthcare settings, extensive testing must be performed to ensure that the system works correctly and fulfills its diagnostic roles and procedures. Another important aspect of the process of creating a tool for accurately diagnosing COVID-19 infectious diseases is the inclusion in the predictive system of a greater number of classes to which the test samples are assigned.The authors intend to continue the research presented in this paper. Within this framework, collecting a larger and more balanced dataset is planned. In addition, the CSDI collection will be expanded to include textural features of patients’ radiological images, which are likely to improve the predictive accuracy of the diagnostic models. To support data collection in a database that integrates diverse diagnostic information, a dedicated computer application will be built. The obtained results can be used to solve the problems of selecting informative features and classifying laboratory and clinical data in medicine and biology.


Artificial intelligence (AI) has the potential to transform medical research by improving disease diagnosis, clinical decision-making, and outcome prediction. Despite the rapid adoption of AI and machine learning (ML) in other domains and industry, deployment in medical research and clinical practice poses several challenges due to the inherent characteristics and barriers of the healthcare sector. Therefore, researchers aiming to perform AI-intensive studies require a fundamental understanding of the key concepts, biases, and clinical safety concerns associated with the use of AI. Through the analysis of large, multimodal datasets, AI has the potential to revolutionize orthopaedic research, with new insights regarding the optimal diagnosis and management of patients affected musculoskeletal injury and disease. The article is the first in a series introducing fundamental concepts and best practices to guide healthcare professionals and researcher interested in performing AI-intensive orthopaedic research studies. The vast potential of AI in orthopaedics is illustrated through examples involving disease- or injury-specific outcome prediction, medical image analysis, clinical decision support systems and digital twin technology. Furthermore, it is essential to address the role of human involvement in training unbiased, generalizable AI models, their explainability in high-risk clinical settings and the implementation of expert oversight and clinical safety measures for failure. In conclusion, the opportunities and challenges of AI in medicine are presented to ensure the safe and ethical deployment of AI models for orthopaedic research and clinical application.Level of evidence IV However, increasingly sophisticated applications in engineering, business, and industrial sectors have shown the rapid technological advancement and maturity of AI, with a growing interest for implementation in medical research, and the healthcare sector [Artificial intelligence (AI) is set to transform the landscape of medical research with innovative approaches to improve disease detection, clinical decision-making, and outcome prediction. The majority of medical research conducted throughout 20e sector , 37. Acce sector , 31. TheIn recent years, the growing availability of healthcare data and the increasing maturity of AI as a technological tool initiated a gradual transformation of the medical research landscape. Patient registries containing granular information about the demographics and therapeutic interventions of numerous patient populations present new avenues for research in the age of big data. Electronic medical records permit the storage and traceability of data collected over the entire duration of medical treatment for patients with different medical conditions, including patient history, physical examination results, diagnostic images, interventions and outcomes over time.Artificial intelligence has the potential to revolutionize medical research by enabling rapid and accurate analysis of vast amounts of data, containing demographic, genetic, clinical, surgical, and rehabilitation-specific information or a combination of these from thousands of patients, in pursuit of patterns associated with specific diseases or conditions. Furthermore, many AI systems possess the advantage of the ability to detect patterns, trends and connections that may not be easily recognized by humans, potentially leading to new clinical insights and breakthroughs in disease prevention, diagnostics and treatment. Analysis of large datasets, often with multimodal data content , would be tedious and inefficient with the statistical methods currently employed in medical research . AnotherApplications of AI can be useful in a broad range of research scenarios with far-reaching potential for clinical utility. The aim of this section is to provide the reader with a broad overview of areas with vast potential in orthopaedics using existing examples from AI-intensive medical research.The continuous growth in the availability of high-quality medical data presents new avenues in the analysis of information derived from the results of clinical trials and national patient registries , 18. OneWhile the clinical implementation of AI-driven predictive algorithms is still in its nascency, their potential is demonstrated by several use cases in the current literature. One notable example is a clinical calculator for ACL revision risk prediction, developed with ML models applied to data from the Norwegian Knee Ligament Registry , 28. WhiImage analysis is perhaps the most well-known application of AI in medicine. The ability of ML algorithms to perform classification and pattern recognition tasks when trained on radiographic images led to the proposal of numerous useful applications across fields, such as histopathology, dermatology, cardiology, ophthalmology, and radiology. Promising applications of AI and imaging technologies in these fields include the detection and grading of prostate cancer based on digitalized prostate biopsy slides , automatIn orthopaedics, AI-based image analysis applications have primarily made an impact on diagnostics, surgical planning, and implant design in traumatology, arthroplasty, and spine surgery. While similar approaches are currently underutilized in sports medicine, momentum is increasing in imaging applications for soft-tissue injury detection. A recent study demonstrated excellent diagnostic performance of an ACL tear detection ML algorithm trained on approximately 20,000 magnetic resonance images (MRI), with similar success after external validation on patient groups from two different countries . SimilarThe broad categories and types of data and ML models have led to advances in the implementation of multimodal AI systems . DespiteCurrently, evaluation of the efficiency and efficacy of medical interventions relies on time-consuming clinical trials, registry studies, and small-scale clinical investigations. While the results of clinical trials are considered the gold-standard of evidence synthesis, the clinical benefit of certain medical interventions may vary among individuals in a population. The digital twin is a concept adopted from engineering, and consists of a virtual representation of a real-world physical entity, such as an object, a system, or a patient . The intThe European AI Act, established in 2022, proposes a risk-based approach to the regulation of AI systems, and characterizes medical applications to be of high-risk [In the context of AI, provenance refers to the origins and history of a particular dataset or model. Provenance comprises information about how the data was collected, who collected it, where it was collected, and any transformations or modifications that were applied to it. Provenance is important in AI because it can help ensure that the data and models being used are reliable, trustworthy and can also help identify potential biases or errors in the data. Provenance in AI-based medical research is essential to build the trust required for clinical implementation of decision support systems and prediction tools, and to enable the design of replicable and transparent studies using AI. A hypothetical clinical decision support system designed to help clinicians optimize the treatment of patients with anterior cruciate ligament (ACL) injury can serve as an example to illustrate the role of provenance. Research studies for testing the validity of such a system will need to disclose the origin of the data the AI model was trained on, including the characteristics of the population, the types of variables collected, the timeframe of data collection, potential sources of bias, to name a few. Furthermore, the decision support system will require a detailed description of the data processing pipeline, model selection process, statistical analysis, methods applied to train, test, and validate models, as well as the parameters used to fine-tune the decision support system. Another important step is to disclose metrics used for the assessment of model performance. While seemingly a tedious task, ensuring provenance is necessary to meet the high standards required for the safe and reliable implementation of AI-driven medical research.One of the major concerns with the ability of AI systems to predict events is that steps taken by certain models to reach predictions are often inaccessible. This characteristic, termed black-box decision making, results in an inability for human observers to explain model output in terms of the original input variables. This feature is particularly problematic for medical applications, as current decision making-systems are based on empirical rules, which allow human interpreters to trace the logic behind reasoning that leads to a certain outcome. This currently accepted and transparent approach enables humans to learn from systems, and perhaps as importantly, to detect and rectify errors and biases in the system, which may otherwise lead to false conclusions and even dangerous consequences. While methods have been proposed to improve the explainability of ML models, their implementation may not be feasible with all data types. Consequently, future AI-intensive medical research should focus on enhanced human interpretability, with the conversion of insight provided by the model to tangible knowledge that mirrors those of medical experts, with potential avenues for error detection. White-box ML models, aptly named to show the contrast to black-box models, provide a broken-down explanation of the steps taken to reach a conclusion with insights about how the input data was used throughout the decision process . This feAs previously discussed, training AI systems on high-quality datasets is a major requirement for clinical adaptation. However, even models trained with the most attention to detail and with carefully curated data may not be universally applicable to every clinical setting. What happens when AI systems encounter unexpected changes in clinical context? Some examples of this phenomenon may be obvious, such as the erroneous prediction of ACL rerupture risk in female downhill skiers by a system that was trained predominantly on male football players. However, a more subtle example may be the poor reproducibility of ACL rerupture risk prediction in patients from one country based on a model trained on registry data from another, with different demographics, injury profiles, and surgical techniques. The inability of AI systems to adapt to new situations, termed distributional shift, is a central problem for the universal application of models across different settings, and may be influenced by countless forms of selection bias that are difficult for researchers to foresee . Recent Recent developments towards standardizing the reports of AI-intensive research include the CONSORT-AI , STARD-AWhile the deployment of AI systems opens exciting possibilities in medical research, mitigation of the potential risks of false predictions will be an essential task in the ensuing years. Navigation between models that produce truthful versus misleading outputs may present unique challenges. An important question is the role of human involvement in the training phase of models used in AI systems. While medical research is heavily rooted in evidence-based thinking and expert consensus, it is also prone to human error and bias. Consequently, excessive human supervision in AI-driven research may force AI systems to make errors akin to those made by human reasoning. However, it is also clear that black-box models preclude the explainability required for the implementation of AI systems in high-risk clinical settings . While cThis presents an important dilemma with practical and philosophical implications. One approach to solving complex research questions is to entrust models built on ground truths founded on human clinical knowledge and existing evidence. The advantage of such supervised learning is that truths are derived using representations comprehensible to humans, which in turn allows human assessment for correctness. Alternatively, certain models are capable of a more intuitive approach, with ground truths based on implicitly derived knowledge by the model, without human supervision of the learning process. In turn, an unsupervised learning approach can provide the benefit of superior pattern recognition and complex, intuitive reasoning at the cost of human interpretability and assessment of the clinical relevance in the underlying logic. Future research will be required to reconcile supervised and unsupervised approaches in medical AI system development, and to ensure explainability and truthfulness .While the recent application of large language models, such as ChatGPT by Open AI , 35 and A fundamental technical introduction to AI and ML for orthopaedic researchers, with a focus on the potential approaches to be used in medical research.Familiarity with the current state of AI in medical research and understanding of the potential benefit conferred by AI in orthopaedics.Approaching hypotheses and research questions in orthopaedic research using AI methods and requirements for interdisciplinary collaboration.Data management for AI-driven orthopaedic research projects.Understanding and interpreting the output of ML-models and AI systems.End-product verification, safety in clinical use, and regulatory concerns.A comprehensive checklist with regards to the previous principles to guide implementation of AI-driven research in orthopaedics.The boundaries of the safe and ethical use of AI in orthopaedic research remain to be determined. In the long-term, over-reliance on AI-driven algorithmic diagnosis, risk-prediction, and prognostics may erode the critical thinking skills considered so essential for clinical medical practice today. Similar to the broad range of industries and scientific domains, careful planning will likely be required to strike the appropriate balance between human- and AI-driven innovation in orthopaedics and sports medicine. While AI will likely exceed human performance in areas such as data analysis, pattern recognition, and decision-making, the goal of clinicians and researchers will be to identify and execute innovative AI-driven applications in medicine and thereby enhance the quality of patient care. The aim of subsequent parts of this learning series is to supply readers with the competence to design and implement AI-driven research projects through proficiency in the following topics: