bluuebunny/crossref_metadata_2025_split huggingface dataset:
Age-related differences in staging, treatment and net survival in relation to frailty in adults with colon cancer in England: an analysis of the COloRECTal cancer data repository (CORECT-R) resource
Authors: Pilleron Sophie , Birch Rebecca J , Taylor John , O'Hanlon Shane , Morris Eva J A 
Published: January 2025

Abstract

Objective
To describe the distribution of disease stages, receipt of major surgery, 1-year net survival (NS) and 1-year conditional NS in relation to age and frailty in adults aged ≥50 diagnosed with colon cancer in England.

Methods
We obtained data on adults aged 50–99 diagnosed with colon cancer between 2014 and 2019, followed up through December 2021, from the national population-based COloRECTal cancer Repository. Frailty was assessed using the Secondary Care Administrative Records Frailty (SCARF) index categorised into fit, mild, moderate and severe frailty. Data on major resection were obtained through linkage with Hospital Episode Statistics dataset. Major resection rates were calculated in adults with stage I–III cancer. Descriptive statistics were used as appropriate. One-year NS from cancer diagnosis and 1-year conditional NS were estimated using the Pohar-Perme estimator.

Results
Out of 130 360 individuals (48% females—50% over 75), 48.9% were fit, ranging from 69% in the 50–64 age group to 31% in the 85–99 age group. Over 80% of adults with stage I–III cancer underwent a major resection. This percentage was 58% amongst fit adults aged over 85. One-year NS decreased as age increased across all frailty levels. Differences in NS between the 50–64 age group and the 85–99 age group were reduced in adults who survived beyond 1 year from diagnosis except for severely frail adults.

Conclusion
This population-based study shows that a non-negligible proportion of older adults diagnosed with colon cancer and deemed ‘fit’ through the SCARF did not receive surgery that may impact their survival.
Revolutionising osseous biopsy: the impact of artificial intelligence in the era of personalized medicine
Authors: Isaac Amanda , Klontzas Michail E , Dalili Danoob , Akdogan Asli Irmak , Fawzi Mohamed , Gugliemi Giuseppe , Filippiadis Dimitrios 
Published: January 2025

Abstract
In a rapidly evolving healthcare environment, artificial intelligence (AI) is transforming diagnostic techniques and personalized medicine. This is also seen in osseous biopsies. AI applications in radiomics, histopathology, predictive modelling, biopsy navigation, and interdisciplinary communication are reshaping how bone biopsies are conducted and interpreted. We provide a brief review of AI in image- guided biopsy of bone tumours (primary and secondary) and specimen handling, in the era of personalized medicine. This article explores AI’s role in enhancing diagnostic accuracy, improving safety in biopsies, and enabling more precise targeting in bone lesion biopsies, ultimately contributing to better patient outcomes in personalized medicine. We dive into various AI technologies applied to osseous biopsies, such as traditional machine learning, deep learning, radiomics, simulation, and generative models. We explore their roles in tumour-board meetings, communication between clinicians, radiologists, and pathologists. Additionally, we inspect ethical considerations associated with the integration of AI in bone biopsy procedures, technical limitations, and we delve into health equity, generalizability, deployment issues, and reimbursement challenges in AI-powered healthcare. Finally, we explore potential future developments and offer a list of open-source AI tools and algorithms relevant to bone biopsies, which we include to encourage further discussion and research.
Validation of a fully automated AI system for accurate left ventricular ejection fraction measurement in echocardiography
Authors: Sveric K , Platzek I , Haussig S , Linke A 
Published: January 2025

Abstract

Background
Two-dimensional echocardiography (Echo) is a feasible method for assessing left ventricular (LV) ejection fraction (EF) in clinical routine. However, LVEF assessment is dependant on the user’s expertise and is time consuming.

Purpose
We aimed to compare the accuracy of an artificial intelligence (AI) system in evaluating Echo exams and calculating biplane LVEF against human interpretation and cardiac computed tomography (CT), which was regarded as the gold standard.

Methods
A diverse cohort of patients (n=190) with symptomatic heart valve disease underwent both Echo and cardiac spiral CT on the same day. Biplane LVEF was manually traced online on apical 4- and 2-chamber views by experienced cardiologists (Human). After completion of the exam,a novel vendor-neutral AI system performed both automated evaluation of Echo exams, appropriate view selection, and calculations of biplane LVEF in one workflow (Figure 1A). LVEF results from CT were supervised by an experienced radiologist (&gt;10 years) (Figure 1B). Pearson’s correlation coefficient (R), regression analysis, and mean absolute error (MAE) were used to assess the agreement of LVEF values between methods.

Results
The final cohort consisted of 182 patients with complete LVEF assessment by the AI (feasibility: 96%). Although LVEF from AI and Human showed good agreement (R=0.79 and MAE= 5.9), AI exhibited higher agreement with CT (R=0.88, MAE=5.1) than Human LVEF calculations with CT (R=0.79, MAE=5.9), as shown in Figures 1C and 1D.

Conclusion
The findings highlight the potential of a novel AI system for assessing biplane LVEF from Echo exams, comparing its performance to human operator-dependent methods and CT as the gold standard. By potentially enhancing accuracy in LVEF measurements, this AI system could represent a viable advancement in the field of Echo, aligning closely with the precision of CT assessments.
Automated artificial intelligence and machine learning based software assists to identify wall motion abnormalities in the left anterior descending territory
Authors: Liel-Cohen N , Yaacobi M , Becher M , Semo S , Sadikov A , Geri O , Ankri Y , Carasso S , Kobal S , Labovitz A 
Published: January 2025

Abstract

Background
Echocardiography is widely used for identification of wall motion abnormalities in patients with suspected coronary artery disease. Segmental wall motion (SWM) is evaluated in practice, qualitatively, by visual estimation. SWM evaluation is known to be complex and time consuming, demands high level of expertise and subjective, especially when providing segmental scores to individual segments. To assess regional rather than global impairment, individual segments, when identified as abnormally contracting, are further evaluated to assess if they are linked to a muscle territory supplied by a specific coronary artery, which may imply impairment in blood supply. We labeled this decision-making process as "Territory logic" (TLG). The Left Anterior Descending (LAD) coronary artery supplies blood to a large mass of the left ventricular muscle, thus, when involved, proximal obstruction is life threatening and early diagnosis may be crucial.
An artificial intelligence (AI) based platform for automated analysis of echocardiographic examination includes a machine learning based module, trained using 735 echo exams, for automated SWM evaluation. The module utilizes the four, two and three chamber apical views and provides segmental scores based on 16 segments model, scoring each segment as normal, hypokinetic or akinetic.

Purpose
To evaluate the ability of an AI based application to identify contractility impairment in the LAD territory compared to visual estimation, when applying TLG to both methods.

Methods
A total of 161 exams collected from two medical centers were included in the study, 64% male, mean age 60±16 years. 48 (29%) had normal LV function, 76 (47%) had previous myocardial infarction. The SWM evaluation by visual estimation was used as reference. Four, two and three chamber apical clips were manually selected from each exam. The module processed the clips automatically. The identification of impairment in the LAD was evaluated by applying TLG to both methods (Fig.1).

Results
143 exams (89%) in which automated processing was possible for all three apical views were included in the analysis. The comparison between the automated and reference results showed a good ability to identify impairment in the LAD by TLG, with specificity and sensitivity of 87% and 80% respectively. The results are summarized in (Table-1).

Conclusions
The AI based platform for automated SWM evaluation can assist in identifying wall motion impairment in the LAD territory, comparable to the logic used in clinical practice.
Automated reading of handheld echocardiograms is feasible and shows strong agreement with high-resolution echocardiography - Results from the PAVE project
Authors: Krieger L , Le Maire M , Pelin D , Chen M , Hitschrich N , Schummers G , Schreckenberg M , Pryss R , Gelbrich G , Karch O , Lengenfelder B , Hu K , Frantz S , Stoerk S , Morbach C 
Published: January 2025

Abstract

Background
We recently showed that population data-based machine-learning can improve the automated echocardiographic quantification of cardiac structure and function. The respective gain in accuracy and precision strengthens the confidence into automated echocardiographic readings and carries potential for applications in various settings.

Purpose
We applied the automated detector to high-resolution standard echocardiograms and to echocardiograms acquired with a handheld device.

Methods and Results
PAVE (Pathology-oriented reading of echocardiography) is a cooperation project between University Hospital Wuerzburg (UKW) and Tomtec Imaging Systems using an established federated machine-learning environment. We recruited 2043 patients (mean age 64±16, 44% women) presenting at the UKW for transthoracic echocardiography (TTE). After high-resolution standard TTE (Vivid E95, GE) trained sonographers acquired images with a handheld ultrasound device (Lumify 2.0, Philips) according to a pre-specified protocol. Images of both modalities were loaded into the analysis platform of the Academic CoreLab Ultrasound-based Cardiovascular Imaging (TomtecArena®, Tomtec, Germany). For the current analysis, we selected n=51 random patients (mean age 65±16, 39% women) from the PAVE cohort. Reading of high-resolution and handheld TTE, respectively, was performed by a trained sonographer (&gt;14 days apart and blinded to the reading results) as well as by the automated detector.
We here present first results regarding 2D (parasternal long axis) measurements of interventricular septum (IVS), left ventricular diameter (LVD) and posterior wall thickness (PW) at end-diastole from manual CoreLab reading (CL) and automated detector reading (AD) of high-resolution TTE (11.3±4.1mm, 10.9±2.5mm; 48.3±8.1mm, 49.5±7.4mm; PW 9.3±3.2mm, 10.0±1.7mm) as well as of the respective handheld echocardiograms (IVS 10.7±3.4mm, 11.0±2.2mm; LVD 47.7±7.4mm, 48.3±6.6mm; PW 9.7±2.7mm, 10.2±1.6mm). The agreement between measurements performed in high-resolution and handheld TTE was assessed using Bland-Altman analysis (Table 1; Figure 1).

Conclusions
The application of an automated detector to handheld TTE was feasible and led to measurement values in the same range but with higher agreement of measurements when compared to human reading. Our results await extension to further echocardiographic parameters and confirmation in larger cohorts but suggest that automated echocardiographic reading might become a valuable tool in patient care. Accurate automated reading of handheld TTE might extend applicability of echocardiography to non-expert settings.
A new paradigm for structural heart disease detection: cost savings of screening with AI-ECG and POCUS
Authors: Schlesinger R , Alexandrino F , Bird J , Kane G , Deshmukh A , Pellikka P , Oh J , Noseworthy P , Lopez-Jimenez F , Friedman P , Pislaru S , Tsaban G 
Published: January 2025

Abstract

Background
AI-enhanced electrocardiogram (AI-ECG) is a cost-effective tool for left ventricular dysfunction screening. However, its cost-effectiveness for other forms of structural heart disease (SHD) is unknown. While AI-ECG is inexpensive, the relatively low positive predictive value (PPV) of many models can lead to high costs from unnecessary follow-up tests like transthoracic echocardiography (TTE). Therefore, strategies to improve the yield of AI-ECG-based screening are needed.

Purpose
To evaluate the cost savings of a stepwise approach to SHD screening using AI-ECG followed by point-of-care ultrasound (POCUS) compared to AI-ECG alone.

Methods
286 adult outpatients undergoing AI-ECG were randomly selected to undergo same-day POCUS. All participants were required to have a recent TTE, our gold standard for SHD diagnosis, for comparison purposes.
We evaluated four SHDs: aortic stenosis, cardiac amyloidosis, hypertrophic cardiomyopathy, and left ventricular dysfunction (defined as left ventricular ejection fraction &lt;50%). The costs of AI-ECG ($75/test) and TTE ($1,305/test) were sourced from Healthcare Bluebook, while the cost of POCUS ($100/test) was independently estimated.
We compared the costs of screening with AI-ECG alone to stepwise screening with AI-ECG followed by POCUS. Costs were analyzed for two paradigms: screening for all forms of SHD in parallel and screening for each SHD individually.

Results
AI-ECG alone identified potential SHD in 125 out of 286 patients. When compared to TTE, only 39 out of 125 were true positives (31% PPV). Among the AI-ECG-positive patients, POCUS demonstrated findings of SHD in 52 out of 125 cases. When compared to TTE, this stepwise approach yielded 32 true positives and 20 false positives (62% PPV).
In the parallel screening paradigm, the cost per patient diagnosed with SHD was $4,733 with AI-ECG alone, which decreased to $3,182 with stepwise screening (33% cost savings). For the individual SHD screening paradigm, cost reductions were as follows: aortic stenosis ($18,724 to $6,315, 66% savings), cardiac amyloidosis ($21,023 to $12,230, 42% savings), hypertrophic cardiomyopathy ($9,883 to $6,175, 38% savings), and left ventricular dysfunction ($4,019 to $3,582, 11% savings).

Conclusions
Stepwise SDH screening with AI-ECG followed by POCUS markedly reduces costs compared to AI-ECG alone. The greatest cost savings for individual SHD screening were observed for aortic stenosis and cardiac amyloidosis, conditions where AI-ECG models have lower PPVs.
Parallel screening for multiple SHDs is also less costly than screening for any individual SHD alone. Therefore, a parallel screening approach may maximize SHD detection while minimizing screening costs.
Stepwise Screening Approach for SHD
SDH Screening Costs
Deep learning in echocardiography: real-time measurements of left ventricular wall thickness and chamber dimensions in the parasternal long-axis view
Authors: Holmstrom V , Smistad E , Ostvik A , Holte E , Stolen S , Lovstakken L , Dalen H , Grenne B 
Published: January 2025

Abstract

Background
Accurate measurements of left ventricular (LV) wall thickness and chamber dimensions in the parasternal long-axis view (PLAX) are essential in the standard echocardiographic examination. Manual assessments introduce variability, and conducting the numerous measurements required for a comprehensive echocardiographic assessment is time-consuming. Therefore, there is potential for improving workflow.

Purpose
To develop a novel deep learning (DL)-based method for fully automated, real-time measurements of LV wall thickness and chamber dimensions while scanning the patient, and to validate its performance in an unselected prospective cohort.

Methods
PLAX recordings from 207 subjects were annotated to train DL networks for cardiac segmentation, timing of end-systole and end-diastole, measurements of LV wall thickness and chamber dimensions. The application presents results from three consecutive cardiac cycles in real-time to the operator for review (Figure 1). Agreement and correlation with reference measurements were evaluated, and the combined acquisition and measurement times for both the novel- and reference methods were recorded.

Results
Feasibility for real-time measurements in PLAX was 98%, with one patient excluded due to poor acoustic window. Agreement with reference measurements was good for interventricular septal diameter (IVSd) (bias - 0.7 mm, limits of agreement (LoA) -4.38 to 2.94 mm), posterior wall thickness (PWd) (bias 0.8 mm, LoA -2.32 to 3.84 mm) and left ventricular internal diastolic diameter (LVIDd) (bias 0.0 mm, LoA -7.0 to 6.9 mm, Figure 2A). Strong correlations were found for IVSd (R=0.84), PWd (R=0.66) and LVIDd (R=0.85), all significant (p &lt;0.001). The average time for acquiring PLAX images was 53 ± 22 seconds, with an additional 42 ± 11 seconds for reference analysis, totaling 94 ± 27 seconds to complete reference PLAX measurements. In contrast, the real-time application took only 59 ± 34 seconds to obtain the same measurements (Figure 2B), resulting in a mean time reduction of 36 seconds (95% CI 24-47 seconds).

Conclusion
This novel DL-based method for fully automated, real-time analysis of key metrics in PLAX is time-efficient, feasible, and yields measurements in good agreement with manual reference. This method has the potential to substantially improve efficiency and precision of PLAX assessment in clinical practice.
Effect of SGLT2i on cardiac remodeling in patients with chronic kidney disease undergoing peritoneal dialysis
Authors: O'neill Gonzalez P , Marco I , Garcia Carro C , Calvo M , Barbieri D , Sanchez Fructuoso A I , Restrepo M A , Olmos C , De Agustin J A , Goirigolzarri J 
Published: January 2025

Abstract

Background/Introduction
Sodium-glucose Cotransporter-2 inhibitors (SGLT2i) are one of the cornerstone therapies for cardiorenal protection in chronic kidney disease (CKD). However, its use has been restricted in patients with very low glomerular filtration rates (GFR). Therefore, evidence of their use in patients with renal replacement therapy (RRT) is limited. Recent data suggest the positive impact of SGLT2i on cardiac remodeling parameters measured by echocardiogram in patients with heart failure.(1) The use of SGLT2i could be beneficial in patients with CKD undergoing peritoneal dialysis as their cardiovascular risk is high and therapies are limited in such scenarios.

Purpose
Our aim was to evaluate changes in echocardiographic parameters of cardiac remodeling in patients with CKD and peritoneal dialysis after the use of SGLT2i.

Methods
Retrospective analysis of a prospective cohort of patients with KDIGO stage 5 CKD receiving peritoneal dialysis. Echocardiographic parameters were measured prior to the introduction of SGLT2i and at a median follow-up of 6.6 months after the initiation of SGLT2i.

Results
11 patients were included, mean age was 69.2 years [9.2] , women represented 36.3% of the total and 54.6% of them had a history of congestive heart failure (HF) admission. (Table 1). The mean left ventricular ejection fraction was preserved (54% [9.9]) but subclinical damage was evident by GLS (-13.8% [4.5]). Left Atrial (LA) dilatation and dysfunction were frequent in the basal study, with a mean LA volume index maximum (LAVI max) of 42 ml/m2 [33.1-66.8] and LA reservoir strain of 20.9% [10.6] (Table 2).
In the follow-up studies, there was a non-significant trend towards a reduction in LA volume at the expense of LAVI reservoir (-7.1ml/m2; p=0.09). However, there was no improvement regarding LA and LV function parameters. The medication was safe and well tolerated, no new HF admissions occurred during the follow-up.

Conclusions
Patients with CKD and peritoneal dialysis have echocardiographic signs of atrial enlargement and dysfunction, as well as subclinical LV damage, with reduced GLS. A non-significant trend of LAVI reservoir reduction was observed with the use of SGLT2i and medication was well tolerated. Larger studies are necessary.
Table 1. Baseline characteristics
Table 2. Echocardiographic parameters
Prevalence, characteristics and predictors of left bundle branch block induced cardiomyopathy following transcatheter aortic valve implantation
Authors: Khoury S , Gvili Perelman M , Avivi I , Tzuberi M , Laufer-Perl M , Banai S , Topilsky Y 
Published: January 2025

Abstract

Background
Left bundle branch block is a relatively common conduction disturbance that can occur as a complication of transcatheter aortic valve implantation (TAVI) procedures. LBBB-induced cardiomyopathy (LBBB-CM) develops due to the mechanical dyssynchrony caused by the conduction abnormality. This dyssynchrony can lead to adverse left ventricular remodeling over time, resulting in a decline in cardiac function. Nevertheless, data on iatrogenic LBBB due to TAVI resulting in cardiomyopathy is lacking.

Purpose
To investigate the prevalence, characteristics and predictors for LBBB-CM following TAVI.

Methods
This was a retrospective single center study. A total of 1792 patients underwent TAVI between 2013 and 2021. New LBBB was documented in medical records in 353 patients. After exclusions (figure 1), we remained with 88 patients who were discharged from hospital with complete LBBB, had no pacemaker in situ and had at least 1 year of follow-up. The median follow-up in this study was 40 [17-64] months. LBBB-CM was defined as an absolute decline of &gt;10% in ejection fraction (EF) due to clear septal dyssynchrony and in the absence of an alternative explanation.

Results
A total of 8 out of 88 patients (9%) developed LBBB-CM over the study period. During the first two years of follow-up, the prevalence was low but gradually increased with extended follow-up. The mean age at discharge from hospital in LBBB-CM group was 83 ± 5 years and the mean EF was 59% ± 3. Patients in the LBBB-CM group had a longer PR interval compared to the no LBBB-CM group (255 ±36 vs 191 ± 38ms, p=0.02) and were more likely to have atrial fibrillation at discharge (50% vs 6%, p&lt;0.001). On Cox regression analysis, both PR interval and QRS duration were identified as significant predictors for the development of LBBB-CM. The HR for PR interval was 1.04 (95% CI 1-1.07, p=0.02) and HR for QRS duration was 1.12 (95% CI 1-1.25, p=0.05).

Conclusions
This is the largest study to date focusing on LBBB-CM following TAVI. LBBB-CM complicates about 9% of patients discharged with a new complete LBBB, occuring within a median follow-up of 40 [17-64] months. Electrocardiographic predictors for the development of LBBB-CM include prolonged QRS duration and PR interval at discharge. Closer follow-up may be required in this group of patients for timely diagnosis and intervention.
Figure