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A deep learning-based multi-view approach to automatic 3D landmarking and deformity assessment of lower limb.
Sci Rep
January 2025
School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Anatomical Landmark detection in CT-Scan images is widely used in the identification of skeletal disorders. However, the traditional process of manually detecting anatomical landmarks, especially in three dimensions, is both time-consuming and prone to human errors. We propose a novel, deep-learning-based approach to automatic detection of 3D landmarks in CT images of the lower limb.
View Article and Find Full Text PDFAutomate cancer synoptic reporting with HyperText Markup Language (HTML) and JavaScript.
Am J Clin Pathol
January 2025
Department of Pathology, Saint Louis University, St Louis, MO, US.
Objectives: The College of American Pathologists (CAP) Cancer Protocols are developed to facilitate cancer synoptic reporting. CAP offers these Cancer Protocols in both free printable and commercially licensed electronic formats. Several academic institutions have also implemented these Cancer Protocols as web-based services.
View Article and Find Full Text PDFEvaluation of ChatGPT-4 for the detection of surgical site infections from electronic health records after colorectal surgery: A pilot diagnostic accuracy study.
J Infect Public Health
December 2024
Department of Surgery, Hospital del Mar, Barcelona, Spain. Electronic address:
Background: Surveillance of surgical site infection (SSI) relies on manual methods that are time-consuming and prone to subjectivity. This study evaluates the diagnostic accuracy of ChatGPT for detecting SSI from electronic health records after colorectal surgery via comparison with the results of a nationwide surveillance programme.
Methods: This pilot, retrospective, multicentre analysis included 122 patients who underwent colorectal surgery.
Automatic ovarian follicle detection using object detection models.
Sci Rep
December 2024
INRAE, CNRS, Université de Tours, PRC, Nouzilly, 37380, France.
Ovaries are of paramount importance in reproduction as they produce female gametes through a complex developmental process known as folliculogenesis. In the prospect of better understanding the mechanisms of folliculogenesis and of developing novel pharmacological approaches to control it, it is important to accurately and quantitatively assess the later stages of ovarian folliculogenesis (i.e.
View Article and Find Full Text PDFGeneralizability, robustness, and correction bias of segmentations of thoracic organs at risk in CT images.
Eur Radiol
December 2024
Department of Radiology, Antoni van Leeuwenhoek-The Netherlands Cancer Institute, Amsterdam, The Netherlands.
Objective: This study aims to assess and compare two state-of-the-art deep learning approaches for segmenting four thoracic organs at risk (OAR)-the esophagus, trachea, heart, and aorta-in CT images in the context of radiotherapy planning.
Materials And Methods: We compare a multi-organ segmentation approach and the fusion of multiple single-organ models, each dedicated to one OAR. All were trained using nnU-Net with the default parameters and the full-resolution configuration.
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