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Retrospective Studies

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An explainable web application based on machine learning for predicting fragility fracture in people living with HIV: data from Beijing Ditan Hospital, China.

Frontiers in cellular and infection microbiology
PURPOSE: This study aimed to develop and validate a novel web-based calculator using machine learning algorithms to predict fragility fracture risk in People living with HIV (PLWH), who face increased morbidity and mortality from such fractures.

Development and evaluation of a deep learning framework for pelvic and sacral tumor segmentation from multi-sequence MRI: a retrospective study.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: Accurate segmentation of pelvic and sacral tumors (PSTs) in multi-sequence magnetic resonance imaging (MRI) is essential for effective treatment and surgical planning.

Artificial intelligence-based automated surgical workflow recognition in esophageal endoscopic submucosal dissection: an international multicenter study (with video).

Surgical endoscopy
BACKGROUND: Endoscopic submucosal dissection (ESD) is a crucial yet challenging multi-phase procedure for treating early gastrointestinal cancers. This study developed an artificial intelligence (AI)-based automated surgical workflow recognition mode...

Do Treatment Choices by Artificial Intelligence Correspond to Reality? Retrospective Comparative Research with Necrotizing Enterocolitis as a Use Case.

Medical decision making : an international journal of the Society for Medical Decision Making
BackgroundIn cases of surgical necrotizing enterocolitis (NEC), the choice between laparotomy (LAP) or comfort care (CC) presents a complex, ethical dilemma. A behavioral artificial intelligence technology (BAIT) decision aid was trained on expert kn...

An interpretable machine learning model based on computed tomography radiomics for predicting programmed death ligand 1 expression status in gastric cancer.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: Programmed death ligand 1 (PD-L1) expression status, closely related to immunotherapy outcomes, is a reliable biomarker for screening patients who may benefit from immunotherapy. Here, we developed and validated an interpretable machine l...

Deep learning radiomics for the prediction of epidermal growth factor receptor mutation status based on MRI in brain metastasis from lung adenocarcinoma patients.

BMC cancer
BACKGROUND: Early and accurate identification of epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patients with brain metastases is critical for guiding targeted therapy. This study aimed to develop a deep...

Development and validation of machine learning models for predicting extubation failure in patients undergoing cardiac surgery: a retrospective study.

Scientific reports
Patients with multiple comorbidities and those undergoing complex cardiac surgery may experience extubation failure and reintubation. The aim of this study was to establish an extubation prediction model using explainable machine learning and identif...