AIMC Topic: Retrospective Studies
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Predicting Robotic Hysterectomy Incision Time: Optimizing Surgical Scheduling with Machine Learning.
JSLS : Journal of the Society of Laparoendoscopic Surgeons
Jan 17, 2025
BACKGROUND AND OBJECTIVES: Operating rooms (ORs) are critical for hospital revenue and cost management, with utilization efficiency directly affecting financial outcomes. Traditional surgical scheduling often results in suboptimal OR use. We aim to b...
Machine learning-based prediction for incidence of endoscopic retrograde cholangiopancreatography after emergency laparoscopic cholecystectomy: A retrospective, multicenter cohort study.
Surgical endoscopy
Jan 16, 2025
BACKGROUND: Laparoscopic cholecystectomy is the preferred treatment for symptomatic cholelithiasis and acute cholecystitis, with increasing applications even in severe cases. However, the possibility of postoperative endoscopic retrograde cholangiopa...
Enhancing Small-for-Gestational-Age Prediction: Multi-Country Validation of Nuchal Thickness, Estimated Fetal Weight, and Machine Learning Models.
Prenatal diagnosis
Jan 16, 2025
OBJECTIVE: The first objective is to develop a nuchal thickness reference chart. The second objective is to compare rule-based algorithms and machine learning models in predicting small-for-gestational-age infants.
A recurrence model for non-puerperal mastitis patients based on machine learning.
PloS one
Jan 16, 2025
OBJECTIVE: Non-puerperal mastitis (NPM) is an inflammatory breast disease affecting women during non-lactation periods, and it is prone to relapse after being cured. Accurate prediction of its recurrence is crucial for personalized adjuvant therapy, ...
Development and validation of an interpretable machine learning model for predicting left atrial thrombus or spontaneous echo contrast in non-valvular atrial fibrillation patients.
PloS one
Jan 16, 2025
PURPOSE: Left atrial thrombus or spontaneous echo contrast (LAT/SEC) are widely recognized as significant contributors to cardiogenic embolism in non-valvular atrial fibrillation (NVAF). This study aimed to construct and validate an interpretable pre...
Novel Machine-Learning Modeling of Facial Trauma Volume With Regional Event and Weather Data.
Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
Jan 15, 2025
OBJECTIVE: Facial trauma volume is difficult to predict accurately. We aim to understand the capacity of climate and regional events to predict daily facial trauma volume. This can provide epidemiologic understanding and subsequently tailor workforce...
Clinical validation of explainable AI for fetal growth scans through multi-level, cross-institutional prospective end-user evaluation.
Scientific reports
Jan 15, 2025
We aimed to develop and evaluate Explainable Artificial Intelligence (XAI) for fetal ultrasound using actionable concepts as feedback to end-users, using a prospective cross-center, multi-level approach. We developed, implemented, and tested a deep-l...
Development of an interpretable machine learning model based on CT radiomics for the prediction of post acute pancreatitis diabetes mellitus.
Scientific reports
Jan 15, 2025
This study sought to establish and validate an interpretable CT radiomics-based machine learning model capable of predicting post-acute pancreatitis diabetes mellitus (PPDM-A), providing clinicians with an effective predictive tool to aid patient man...
Machine learning analysis of cervical balance in early-onset scoliosis post-growing rod surgery: a case-control study.
Scientific reports
Jan 15, 2025
We aimed to analyze the cervical sagittal alignment change following the growing rod treatment in early-onset scoliosis (EOS) and identify the risk factors of sagittal cervical imbalance after growing-rod surgery of machine learning. EOS patients fro...
A Supervised Explainable Machine Learning Model for Perioperative Neurocognitive Disorder in Liver-Transplantation Patients and External Validation on the Medical Information Mart for Intensive Care IV Database: Retrospective Study.
Journal of medical Internet research
Jan 15, 2025
BACKGROUND: Patients undergoing liver transplantation (LT) are at risk of perioperative neurocognitive dysfunction (PND), which significantly affects the patients' prognosis.
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