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AI predictive models and advancements in microdissection testicular sperm extraction for non-obstructive azoospermia: a systematic scoping review.
Hum Reprod Open
November 2024
Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Study Question: How accurately can artificial intelligence (AI) models predict sperm retrieval in non-obstructive azoospermia (NOA) patients undergoing micro-testicular sperm extraction (m-TESE) surgery?
Summary Answer: AI predictive models hold significant promise in predicting successful sperm retrieval in NOA patients undergoing m-TESE, although limitations regarding variability of study designs, small sample sizes, and a lack of validation studies restrict the overall generalizability of studies in this area.
What Is Known Already: Previous studies have explored various predictors of successful sperm retrieval in m-TESE, including clinical and hormonal factors. However, no consistent predictive model has yet been established.
Bankart lesions, or anterior-inferior glenoid labral tears, are diagnostically challenging on standard MRIs due to their subtle imaging features-often necessitating invasive MRI arthrograms (MRAs). This study develops deep learning (DL) models to detect Bankart lesions on both standard MRIs and MRAs, aiming to improve diagnostic accuracy and reduce reliance on MRAs. We curated a dataset of 586 shoulder MRIs (335 standard, 251 MRAs) from 558 patients who underwent arthroscopy.
View Article and Find Full Text PDFBackground: Limited-angle (LA) dual-energy (DE) cone-beam CT (CBCT) is considered as a potential solution to achieve fast and low-dose DE imaging on current CBCT scanners without hardware modification. However, its clinical implementations are hindered by the challenging image reconstruction from LA projections. While optimization-based and deep learning-based methods have been proposed for image reconstruction, their utilization is limited by the requirement for X-ray spectra measurement or paired datasets for model training.
View Article and Find Full Text PDFA systematic review on the application of precision livestock farming technologies to detect lying, rest and sleep behavior in dairy calves.
Front Vet Sci
December 2024
Department of Veterinary Medicine and Animal Sciences, University of Milan, Lodi, Italy.
Welfare studies are increasingly involving the application of Precision Livestock Farming (PLF) sensors, rather than the use of animal-based indicators directly assessed. PLF technology has the advantage to constantly monitor behavior over a long period of time, thus enabling the assessor to identify changes in animal time budgets in real-time. In calves, lying behavior is essential: new-borns have been reported to spend 70-80% of their daily time lying.
View Article and Find Full Text PDFRobust and interpretable AI-guided marker for early dementia prediction in real-world clinical settings.
EClinicalMedicine
August 2024
Department of Psychology, University of Cambridge, Cambridge, CB2 3EB, United Kingdom.
Background: Predicting dementia early has major implications for clinical management and patient outcomes. Yet, we still lack sensitive tools for stratifying patients early, resulting in patients being undiagnosed or wrongly diagnosed. Despite rapid expansion in machine learning models for dementia prediction, limited model interpretability and generalizability impede translation to the clinic.
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