syedzayyan/paper_mill_benchmark huggingface dataset:
Bioinformatic Characterization of Whole Blood Neutrophils in Pelvic Inflammatory Disease: A Potential Prognostic Indicator for Transumbilical Single-Port Laparoscopic Pelvic Abscess Surgery

The purpose of this research is to determine the prognosis of patients treated with transumbilical single-port laparoscopic surgery for acute pelvic inflammatory illness.Postoperative data on 129 patients treated with laparoscopic surgery for acute pelvic inflammatory illness were obtained retrospectively.It was observed that the shorter the time required for postoperative leukocyte recovery to normal, the shorter the time required for postoperative pain and diet recovery, as well as hospital stay, in such individuals.Cibersort was used to examine patient data from geo.The most significant difference between the normal and pelvic inflammatory groups was in neutrophil content.Association study found a substantial positive correlation between the quantity of neutrophils infiltrating the immune system and the abundance of monocyte m0 infiltrating the immune system.Neutrophil immune infiltration was strongly inversely linked with plasma cells, activated cd8+ tm cells, and active cd4+ tm cells.Four m rnas linked with pelvic inflammatory illness were revealed to be strongly associated with neutrophil immune infiltration, notably calml4, coq10b, dcps, and ppp2r1a.The roc revealed that calml4 (area under the curve (auc): 0.769, 95% confidence interval (ci): 0.638–0.881), coq10b (auc: 0.742, 95% ci: 0.587–0.881), ppp2r1a (auc: 0.733 95% ci: 0.593–0.857), and dcps (auc: 0.745, 95% ci: 0.571–0.900) were potential markers for predicting pelvic inflammatory disease.Calml4, coq10b, ppp2r1a, and dcps may be critical determinants determining the amount of preoperative neutrophil infiltration and the time required for leukocyte recovery after single-port laparoscopy in acute pelvic inflammatory illness.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
Bayesian Analysis of Trends in Utilization of Maternal Healthcare Services in Pakistan during 2006-2018

S.This study is aimed at investigating the time trends and disparities in access to maternal healthcare in pakistan using bayesian models.Study design.A longitudinal study from 2006 to 2018.Methods.The detailed analysis is based on the data from pakistan demographic and health survey (pdhs) conducted during 2006-2018.We have proposed bayesian logistic regression models (blrm) to investigate the trends of maternal healthcare in the country.Based on different goodness-of-fit criteria, the performance of proposed models has also been compared with repeatedly used classical logistic regression models (clrm).Results.The results from the analysis suggested that blrm perform better than clrm.The access to antenatal healthcare increased from 61% to 86% during years 2006-18.The utilization of medication also improved from 44% in 2006 to 60% in 2018.Despite the improvements from 2006 to 2018, every three out of ten women were not protected against neonatal tetanus, neither delivered in the health facility place nor availed with the skilled health provider at the time of delivery during 2018.Similarly, two-fifth mothers did not received any skilled postnatal checkup within two days after delivery.Additionally, the likelihood of mhs provided to mothers is in favor of mothers with lower ages, lower birth orders, urban residences, higher education, higher wealth quintiles, and residents of sindh and punjab.Conclusions.The gaps in utilization of mhs in different socioeconomic groups of the society have not decreased significantly during 2006-2018.Any future maternal health initiative in the country should focus to reduce the observed disparities among different socioeconomic sectors of the society.

Journal: Computational and Mathematical Methods in Medicine (2021)
Publication types: Research Article;
Automated Diagnosis and Localization of Melanoma from Skin Histopathology Slides Using Deep Learning: A Multicenter Study

In traditional hospital systems, diagnosis and localization of melanoma are the critical challenges for pathological analysis, treatment instructions, and prognosis evaluation particularly in skin diseases.In literature, various studies have been reported to address these issues; however, a prominent smart diagnosis system is needed to be developed for the smart healthcare system.In this study, a deep learning-enabled diagnostic system is proposed and implemented that it has the capacity to automatically detect malignant melanoma in whole slide images (wsis).In this system, the convolutional neural network (cnn), sophisticated statistical method, and image processing algorithms were integrated and implemented to locate benign and malignant lesions which are extremely useful in the diagnoses process of melanoma disease.To verify the exceptional performance of the proposed scheme, it is implemented in a multicenter database, which has 701 wsis (641 wsis from central south university xiangya hospital (csuxh) and 60 wsis from the cancer genome atlas (tcga)).Experimental results have verified that the proposed system has achieved an area under the receiver operating characteristic curve (auroc) of 0.971.Furthermore, the lesion area on the wsis is represented by its degree of malignancy.These results show that the proposed system has the capacity to fully automate the diagnosis and localization problem of the melanoma in the smart healthcare systems.

Journal: Journal of Healthcare Engineering (2021)
Publication types: Research Article;
Artificial Intelligence Technology Combined with Ultrasound-Guided Needle Knife Interventional Treatment of PF: Improvement of Pain, Fascia Thickness, and Ankle-Foot Function in Patients

To explore the effect of artificial intelligence (ai) technology combined with ultrasound-guided needle knife intervention in the treatment of plantar fasciitis (pf) on pain, fascia thickness, and ankle and foot function.Methods.A total of 130 patients with pf treated in our hospital from january 2019 to april 2021 were enrolled.The patients were randomly assigned into the control group and the study group.The control group received ultrasound-guided needle knife interventional therapy, and the study group received ai technology combined with ultrasound-guided needle knife interventional therapy.The curative effect, vas score, plantar fascia thickness, plantar fascia elasticity score, plantar fascia blood flow index, and aofas score were investigated.Results.The total effective rate of the study group was higher compared to that of the control ( p &lt; 0.05 ).There exhibited no significant difference in vas score before treatment, but the vas score of the study group was lower compared to that of the control group at 2, 4, and 8 weeks after treatment.There exhibited no significant difference in plantar fascia thickness before treatment ( p &gt; 0.05 ), but after treatment, the plantar fascia thickness in the study group was lower compared to that in the control ( p &lt; 0.05 ).The plantar fascia thickness in the study group was lower compared to that in the control at 2, 4, and 8 weeks after treatment ( p &lt; 0.05 ).In terms of the plantar fascia elasticity score, there exhibited no significant difference before treatment ( p &gt; 0.05 ), but the plantar fascia elasticity score of the study group was lower compared to that of the control at 2, 4, and 8 weeks after treatment ( p &lt; 0.05 ).There exhibited no significant difference in plantar fascia blood flow index before treatment ( p &gt; 0.05 ), but after treatment, the plantar fascia blood flow index in the study group was higher compared to that in the control ( p &lt; 0.05 ).The plantar fascia blood flow index in the study group was higher compared to that in the control at 2, 4, and 8 weeks after treatment ( p &lt; 0.05 ).There exhibited no significant difference in the aofas score before treatment, but after treatment, the aofas score of the study group was higher compared to that of the control at 2, 4, and 8 weeks after treatment.Conclusion.Patients with pf receive ai technology combined with ultrasound-guided needle knife interventional therapy, which can effectively relieve pain and improve fascia thickness and ankle-foot function.Thus, ai technology combined with ultrasound-guided needle knife interventional therapy has the advantages of convenient operation, safety, and effectiveness, which is worthy of clinical application.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Clinical Study;
Artificial Intelligence Algorithm-Based MRI in Evaluating the Treatment Effect of Acute Cerebral Infarction

The study is aimed at exploring the application of artificial intelligence algorithm-based magnetic resonance imaging (mri) in the diagnosis of acute cerebral infarction, expected to provide a reference for diagnosis and effect evaluation of acute cerebral infarction.In this study, 80 patients diagnosed with suspected acute cerebral infarction per diagnostic criteria for cerebral infarction were selected as the research subjects.Mri images were reconstructed by deep dictionary learning to improve their recognition ability.At the same time, the same diagnostic operation was performed by computed tomography (ct) images to compare with mri.The results of the interalgorithm comparison showed the image reconstruction effect of the deep dictionary learning model is significantly better than sae reconstruction, single-layer dictionary reconstruction model, and kavd reconstruction.After comparison, the results of mri based on artificial intelligence algorithm and ct evaluation were statistically significant ( p &lt; 0.05 ).In the lesion image, the diameter of mri lesions ( 3.81 ± 0.77 cm) based on artificial intelligence algorithm and the diameter of lesions in ct ( 3.66 ± 1.65 cm) also had significant statistical significance ( p &lt; 0.05 ).The results showed that mri based on deep learning was more sensitive than ct imaging for diagnosis and evaluation of patients with acute cerebral infarction, with only 1 case misdiagnosed.The rate of disease detection and lesion image quality had a higher improvement.The results can provide effective support for the clinical application of mri based on artificial intelligence algorithm in the diagnosis of acute cerebral infarction.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
Artificial Intelligence Algorithm-Based CTA Imaging for Diagnosing Ischemic Type Biliary Lesions after Orthotopic Liver Transplantation

The study focused on the clinical application value of artificial intelligence-based computed tomography angiography (cta) in the diagnosis of orthotopic liver transplantation (olt) after ischemic type biliary lesions (itbl).A total of 66 patients receiving olt in hospital were selected.Convolutional neural network (cnn) algorithm was used to denoise and detect the edges of cta images of patients.At the same time, the quality of the processed image was subjectively evaluated and quantified by hmax, ur, cr, and other indicators.Then, the digital subtraction angiography (dsa) diagnosis and cta diagnosis based on cnn were compared for the sensitivity, specificity, positive predictive value, negative predictive value, and patient classification results.It was found that cta can clearly reflect the information of hepatic aorta lesions and thrombosis in patients with ischemic single-duct injury after liver transplantation.After neural network algorithm processing, the image quality is obviously improved, the lesions are more prominent, and the details of lesion parts are also well displayed.Itbl occurred in 40 (71%) of 56 patients with abnormal cta at early stage.Itbl occurred in only 8 (12.3%) of 65 patients with normal cta at early stage.Early cta manifestations had high sensitivity (72.22%), specificity (87.44%), positive predictive value (60.94%), and negative predictive value (92.06%) for the diagnosis of itbl.It was concluded that artificial intelligence-based cta had high clinical application value in the diagnosis of itbl after olt.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
Artificial Intelligence Algorithm-Based Computed Tomography Image of Both Kidneys in Diagnosis of Renal Dysplasia

The objective of this study was to explore the accuracy of low-dosage computed tomography (ct) images based on the expectation maximization algorithm denoising algorithm (em algorithm) in the detection and diagnosis of renal dysplasia, so as to provide reasonable research basis for accuracy improvement of clinical diagnosis of renal dysplasia.120 patients with renal dysplasia in hospital were randomly selected as the research objects, and they were divided into two groups by random number method, with 60 patients in each group.The low-dosage ct images of patients in the control group were not processed (nonalgorithm group), and the low-dosage ct images of patients in the observation group were denoised using the em algorithm (algorithm group).In addition, it was compared with the results of the comprehensive diagnosis (gold standard) to analyze the accuracy of the diagnosis of the two groups of patients and the consistency with the results of the pathological diagnosis.The results were compared with those of the comprehensive diagnosis (gold standard) to analyze the accuracy of the diagnosis of the two groups of patients.The results showed that the peak signal-to-noise ratio (psnr) (15.9 d b) of the em algorithm was higher than the regularized adaptive matching pursuit (ramp) algorithm (1.69 d b) and the mean filter (4.3 d b) ( p &lt; 0.05 ).The time consumption of em algorithm (21 s) was shorter than that of pwls algorithm (34 s) and ms-pwls algorithm (39 s) ( p &lt; 0.05 ).The diagnosis accuracy of dysplasia of single kidney, absence of single kidney, horseshoe kidney, and duplex kidney was obviously higher in the algorithm group than the control group ( p &lt; 0.05 ), which were 66.67% vs.90%, 60% vs.88.89%, 71.42% vs.100%, and 60% vs.88.89%, respectively.The incidence of hypertension in patients with autosomal dominant polycystic kidney disease (adpkd) (56.77%) was much higher than that of the other diseases ( p &lt; 0.05 ).After denoising by the em algorithm, low-dosage ct image could improve the diagnostic accuracy of several types of renal dysplasia except adpkd, showing certain clinical application value.In addition, adpkd was easy to cause hypertension.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
Application Research of Artificial Intelligence Screening System for Diabetic Retinopathy

According to the latest data from the bureau of disease control and prevention of the national health and family planning commission, china currently has 199.6 million diabetic patients and has become the world’s largest country with diabetes.The prevalence rate is as high as 14.3%, which is much higher than the world average of 5.8%.The primary-level ophthalmic screening service is one of the important tasks to improve primary-level medical services, and the corresponding ophthalmic imaging diagnosis technology is an important support for primary-level medical and health services.Therefore, it is very necessary for us to study the application of artificial intelligence image recognition technology for diabetic retinopathy under the medical consortium mode and to study the precise initial diagnosis, precise referral, and precise follow-up of diabetic retina under the medical conjoined mode, so as to better promote the transformation of the ophthalmology primary service model.Based on this background, in this article, we have proposed and carried out the following solution: (1) diabetes data collation.Based on medical artificial intelligence technology, this paper collected 2,265 electronic medical records from an eye hospital in ningbo and selected 2,000 qualified medical records for data integration and preprocessing.The contents of electronic medical records mainly include age, gender, and examination records.(2) establish diabetic retinopathy diagnosis model based on neural network algorithm.This article first uses the classic algorithm of bp neural network for modeling, chooses the levenberg–marquardt method as the training function, and selects 10 hidden layer units through comparison experiments.After that, ophthalmologists assessed 80 sets of test results and determined the right diagnosis rate.Finally, this article compares and analyzes the accuracy of the two routes in 80 tests.

Journal: Journal of Healthcare Engineering (2022)
Publication types: Research Article;
Application of Ultrasonic Intelligent Imaging in L-Selectin Regulating Embryo Implantation in Mongolian Sheep Endometrium

In order to explore the practical application of ultrasonic imaging in the pregnancy stage of mongolian sheep and the role of l-selectin in the embryo implantation process of mongolian sheep, this paper systematically observed the early embryonic development by b-mode ultrasonic imaging wave diagnostic instrument with 5 mhz rectal probe and detected the expression of s lex and l-selectin in embryonic cells (jar cells) and endometrial cells (rl95-2 cells) by immunoassay to show the role of l-selectin in embryonic adhesion.The results were as follows: the correct rate of fetal sex determination by ultrasound imaging increased with the increase of pregnancy days and reached 93% at 84 days; s lex/l-selectin on the surface of jar/rl95-2 cells is involved in the adhesion between embryo and endometrium; and when the concentration of l-selectin was 30 μg/ml, the implantation success rate of fertilized eggs and embryos was the highest, reaching 95%.It is proved that ultrasonic intelligent imaging exploration can summarize the imaging characteristics of the early development law of sheep fetus, which provides a basis for b-ultrasound to monitor fetal growth and predict fetal age.While discussing the molecular mechanism of implantation, it provides a new idea and means for the clinical intervention of contraception and pregnancy assistance with oligosaccharide as the target.

Journal: Scanning (2022)
Publication types: Research Article;
Application of Intelligent Image Color Technology in Teaching Chinese Painting Color

This paper presents an in-depth analysis and research on the application of intelligent image color technology in teaching chinese painting color.A feature reorganization-based approach to chinese painting image style migration is proposed.Using the connection between depth features in neural networks and image semantics, the artistic style expression method of traditional chinese painting is combined with existing methods to match the feature matrix in the generative network by feature reorganization, and finally, the migrated image is generated using a decoder.The texture of the target image is input to the generator as an additional condition, and the discriminator is changed to a relativistic discriminator to build a new generative adversarial network.To demonstrate the effectiveness of the method, a large number of chinese ink and wash style landscape paintings and natural landscape photos are used as experimental data.The experimental results show that the obtained generated images after style migration are 8% better in realism and quality.An image color intelligent analysis system was designed, which integrates a color classification module, a pigment analysis module, an output saving module, and a pigment sample library to achieve the automatic classification of different color areas in chinese painting, and ultimately allows a judgment to be made on the most likely pigment to be used for each type of color.In the teaching of chinese painting, the system can transform the traditional “ear-listening” color transmission through vision into eye perception, which makes the teaching of chinese painting vivid, arouses the enthusiasm of the classroom, and greatly promotes the color teaching of chinese painting.

Journal: Security and Communication Networks (2022)
Publication types: Research Article;
Application of Face Recognition in E-commerce Security Authentication in the Era of Big Data

Although internet technology brings invaluable benefits to all walks of life, the security of network and information is becoming more and more prominent.The leakage of personnel information caused by internet security incidents causes irreparable harm and loss to individuals or enterprises.E-commerce is a virtual transaction mode based on internet technology.Its security requirements for transactions are more stringent than those for traditional transaction modes.Traditional identity authentication technology can no longer meet its security needs.People urgently need a reliable identity authentication system, meaning to ensure the security of e-commerce.According to the actual application scenarios of the algorithm in e-commerce, this paper, based on the research on face recognition technology, focuses on the in-depth research on face detection technology under the background of big data in order to introduce face recognition technology in this paper.This paper proposes a feature-based face matching algorithm.The face image is preprocessed to improve the accuracy of real-time face detection and reduce the false detection rate.Based on the research of traditional facial feature extraction technology, a virtual sample set that can effectively support a traditional facial feature extraction algorithm is constructed to solve the problem of insufficient training samples in practical applications.The experimental results showed that the accuracy of the method in this paper can reach up to 79.5% and that the minimum time consumption is only 0.142 s.Compared with the traditional method, the accuracy rate is higher and the time consumption is shorter.

Journal: Security and Communication Networks (2022)
Publication types: Research Article;
Application of Deep Learning Technology in Predicting the Risk of Inpatient Death in Intensive Care Unit

The intensive care unit (icu) is an important unit for the rescue of critically ill patients in hospitals, and patient mortality is an important indicator to measure the level of icu treatment.Currently, a variety of clinical scoring systems are used to evaluate the patient's condition and predict survival, but these systems require a lot of resources.However, due to the rapid development of artificial intelligence and deep learning, machine learning based methods have been used to study the survival prediction of icu patients.Additionally, these methods have made significant progress, but there is still a distance from clinical application, and equally metric interpretability of the deep learning method is not very mature.Therefore, in this paper, we have proposed a predicting model for the life and death of icu patients, which is primarily based on the fuzzy artmap model.With a thorough analysis of the existing icu patient condition assessment and life and death prediction methods, we have observed that patient’s icu monitoring information performs integrated analysis and extracts features according to the clinical characteristics of physiological indicators.Finally, fuzzy artmap neural network is used to predict the life and death of patients.Likewise, prediction results are combined with the clinical scoring system and logistic regression, artificial neural network, support vector machine, and ada boost.Experimental results of these algorithms were compared, which verifies that the proposed method has outperformed the existing model.The main purpose of the proposed mode is to design a life and death prediction method for icu patients, which has high predictive performance and is an acceptable method for clinical medical staff, where icu monitoring data is used.Experimental results show that the method proposed has achieved better prediction performance and accuracy ratio, which provide theoretical reference for clinical application.

Journal: Journal of Healthcare Engineering (2021)
Publication types: Research Article;