AIMC Topic: Retrospective Studies
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Enhancing decision-making in tubal ectopic pregnancy using a machine learning approach to expectant management: a clinical article.
BMC pregnancy and childbirth
Dec 19, 2024
OBJECTIVE: To refine decision-making regarding expectant management for ectopic pregnancy (EP) using machine learning.
An explainable and supervised machine learning model for prediction of red blood cell transfusion in patients during hip fracture surgery.
BMC anesthesiology
Dec 19, 2024
AIM: The study aimed to develop a predictive model with machine learning (ML) algorithm, to predict and manage the need for red blood cell (RBC) transfusion during hip fracture surgery.
XGBoost-SHAP-based interpretable diagnostic framework for knee osteoarthritis: a population-based retrospective cohort study.
Arthritis research & therapy
Dec 19, 2024
OBJECTIVE: To use routine demographic and clinical data to develop an interpretable individual-level machine learning (ML) model to diagnose knee osteoarthritis (KOA) and to identify highly ranked features.
Evaluation of prediction errors in nine intraocular lens calculation formulas using an explainable machine learning model.
BMC ophthalmology
Dec 19, 2024
BACKGROUND: The purpose of the study was to evaluate the relationship between prediction errors (PEs) and ocular biometric variables in cataract surgery using nine intraocular lens (IOL) formulas with an explainable machine learning model.
Artificial intelligence for better goals of care documentation.
BMJ supportive & palliative care
Dec 19, 2024
OBJECTIVES: Lower rates of goals of care (GOC) conversations have been observed in non-white hospitalised patients, which may contribute to racial disparities in end-of-life care. We aimed to assess how a targeted initiative to increase GOC documenta...
Artificial intelligence-based computer-aided diagnosis for breast cancer detection on digital mammography in Hong Kong.
Hong Kong medical journal = Xianggang yi xue za zhi
Dec 19, 2024
INTRODUCTION: Research concerning artificial intelligence in breast cancer detection has primarily focused on population screening. However, Hong Kong lacks a population-based screening programme. This study aimed to evaluate the potential of artific...
Feasibility/clinical utility of half-Fourier single-shot turbo spin echo imaging combined with deep learning reconstruction in gynecologic magnetic resonance imaging.
Abdominal radiology (New York)
Dec 18, 2024
BACKGROUND: When antispasmodics are unavailable, the periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER; called BLADE by Siemens Healthineers) or half Fourier single-shot turbo spin echo (HASTE) is clinically used...
A multimodal deep-learning model based on multichannel CT radiomics for predicting pathological grade of bladder cancer.
Abdominal radiology (New York)
Dec 18, 2024
OBJECTIVE: To construct a predictive model using deep-learning radiomics and clinical risk factors for assessing the preoperative histopathological grade of bladder cancer according to computed tomography (CT) images.
Textbook outcome in liver surgery for intrahepatic cholangiocarcinoma: defining predictors of an optimal postoperative course using machine learning.
HPB : the official journal of the International Hepato Pancreato Biliary Association
Dec 18, 2024
BACKGROUND: We sought to define textbook outcome in liver surgery (TOLS) for intrahepatic cholangiocarcinoma (ICC) by considering the implications of perioperative outcomes on overall survival (OS).
Development and Validation of a Multimodal Machine Learning Model for Diagnosing and Assessing Risk of Crohn's Disease in Patients With Perianal Fistula.
Alimentary pharmacology & therapeutics
Dec 18, 2024
BACKGROUND: Diagnosis of Crohn's disease (CD) can pose challenges, particularly when perianal fistula is the initial presentation.
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