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Identifying abdominal aortic aneurysm size and presence using Natural Language Processing of radiology reports: a systematic review and meta-analysis.
Abdom Radiol (NY)
January 2025
Kerman University of Medical Sciences, Kerman, Islamic Republic of Iran.
Background And Aim: Prior investigations of the natural history of abdominal aortic aneurysms (AAAs) have been constrained by small sample sizes or uneven assessments of aggregated data. Natural language processing (NLP) can significantly enhance the investigation and treatment of patients with AAAs by swiftly and effectively collecting imaging data from health records. This meta-analysis aimed to evaluate the efficacy of NLP techniques in reliably identifying the existence or absence of AAAs and measuring the maximal abdominal aortic diameter in extensive datasets of radiology study reports.
View Article and Find Full Text PDFAn explainable transformer model integrating PET and tabular data for histologic grading and prognosis of follicular lymphoma: a multi-institutional digital biopsy study.
Eur J Nucl Med Mol Imaging
January 2025
Department of Nuclear Medicine, West China Hospital, Sichuan University, No.37, Guoxue Alley, Chengdu City, Sichuan Province, 610041, China.
Background: Pathological grade is a critical determinant of clinical outcomes and decision-making of follicular lymphoma (FL). This study aimed to develop a deep learning model as a digital biopsy for the non-invasive identification of FL grade.
Methods: This study retrospectively included 513 FL patients from five independent hospital centers, randomly divided into training, internal validation, and external validation cohorts.
Bayesian deep learning applied to diabetic retinopathy with uncertainty quantification.
Heliyon
January 2025
Information Technology Department, Technical College of Informatics-Akre, Akre University for Applied Sciences, Kurdistan Regain, Iraq.
Deep Learning (DL) has significantly contributed to the field of medical imaging in recent years, leading to advancements in disease diagnosis and treatment. In the case of Diabetic Retinopathy (DR), DL models have shown high efficacy in tasks such as classification, segmentation, detection, and prediction. However, DL model's opacity and complexity lead to errors in decision-making, particularly in complex cases, making it necessary to estimate the model's uncertainty in predictions.
View Article and Find Full Text PDFNutrition users' guides: RCTs part 1 - structured guide for assessing risk of bias in randomised controlled trials that address therapy or prevention questions.
BMJ Nutr Prev Health
August 2024
Department of Nutrition, College of Agriculture and Life Sciences, Texas A&M University, College Station, Texas, USA.
The purpose of this article, part 1 of 2 on randomised controlled trials (RCTs), is to provide readers (eg, clinicians, patients, health service and policy decision-makers) of the nutrition literature structured guidance on interpreting RCTs. Evaluation of a given RCT involves several considerations, including the potential for risk of bias, the assessment of estimates of effect and their corresponding precision, and the applicability of the evidence to one's patient. Risk of bias refers to flaws in the design or conduct of a study that may lead to a deviation from measuring the underlying true effect of an intervention.
View Article and Find Full Text PDFThe effect of verdict system on juror decisions: a quantitative meta-analysis.
Psychiatr Psychol Law
January 2024
School of Psychology and Neuroscience, University of Glasgow, Glasgow, UK.
We study the effect of the Scottish three-verdict system (guilty, not guilty, not proven) and the Anglo-American two-verdict system (guilty, not guilty) on juror decisions by combining data sets from 10 mock trials reported in suitable studies. A logistic regression with random effects uses the exact number of convictions and acquittals in 10 mock trials from a total of 1778 jurors to reliably estimate the effect of verdict system. We found a statistically significant verdict effect suggesting that the odds for a conviction by a juror are about 0.
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