AIMC Topic: Retrospective Studies
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Exploring machine learning tools in a retrospective case-study of patients with metastatic non-small cell lung cancer treated with first-line immunotherapy: A feasibility single-centre experience.
Lung cancer (Amsterdam, Netherlands)
Dec 31, 2024
BACKGROUND: Artificial intelligence (AI) models are emerging as promising tools to identify predictive features among data coming from health records. Their application in clinical routine is still challenging, due to technical limits and to explaina...
Leveraging Machine Learning to Identify Subgroups of Misclassified Patients in the Emergency Department: Multicenter Proof-of-Concept Study.
Journal of medical Internet research
Dec 31, 2024
BACKGROUND: Hospitals use triage systems to prioritize the needs of patients within available resources. Misclassification of a patient can lead to either adverse outcomes in a patient who did not receive appropriate care in the case of undertriage o...
Development and evaluation of a deep learning segmentation model for assessing non-surgical endodontic treatment outcomes on periapical radiographs: A retrospective study.
PloS one
Dec 31, 2024
This study aimed to evaluate the performance of a deep learning-based segmentation model for predicting outcomes of non-surgical endodontic treatment. Preoperative and 3-year postoperative periapical radiographic images of each tooth from routine roo...
A novel artificial intelligence segmentation model for early diagnosis of bladder tumors.
Abdominal radiology (New York)
Dec 30, 2024
OBJECTIVE: Despite cystoscopy plays an important role in bladder tumors diagnosis, it often falls short in flat cancerous tissue and minuscule satellite lesions. It can easily lead to a missed diagnosis by the urologist, which can lead to a swift tum...
Machine Learning Prediction of Early Recurrence in Gastric Cancer: A Nationwide Real-World Study.
Annals of surgical oncology
Dec 30, 2024
BACKGROUND: Patients with gastric cancer (GC) who experience early recurrence (ER) within 2 years postoperatively have poor prognoses. This study aimed to analyze and predict ER after curative surgery for patients with GC using machine learning (ML) ...
Noninvasive machine-learning models for the detection of lesion-specific ischemia in patients with stable angina with intermediate stenosis severity on coronary CT angiography.
Physical and engineering sciences in medicine
Dec 30, 2024
This study proposed noninvasive machine-learning models for the detection of lesion-specific ischemia (LSI) in patients with stable angina with intermediate stenosis severity based on coronary computed tomography (CT) angiography. This single-center ...
Validation of a novel tool for automated tooth modelling by fusion of CBCT-derived roots with the respective IOS-derived crowns.
Journal of dentistry
Dec 30, 2024
OBJECTIVES: To validate a novel artificial intelligence (AI)-based tool for automated tooth modelling by fusing cone beam computed tomography (CBCT)-derived roots with corresponding intraoral scanner (IOS)-derived crowns.
Construction of prediction model of early glottic cancer based on machine learning.
Acta oto-laryngologica
Dec 30, 2024
BACKGROUND: The early diagnosis of glottic laryngeal cancer is the key to successful treatment, and machine learning (ML) combined with narrow-band imaging (NBI) laryngoscopy provides a new idea for the early diagnosis of glottic laryngeal cancer.
Machine learning algorithms in constructing prediction models for assisted reproductive technology (ART) related live birth outcomes.
Scientific reports
Dec 30, 2024
Currently applicable models for predicting live birth outcomes in patients who received assisted reproductive technology (ART) have methodological or study design limitations that greatly obstruct their dissemination and application. Models suitable ...
A deep learning-based ADRPPA algorithm for the prediction of diabetic retinopathy progression.
Scientific reports
Dec 30, 2024
As an alternative to assessments performed by human experts, artificial intelligence (AI) is currently being used for screening fundus images and monitoring diabetic retinopathy (DR). Although AI models can provide quasi-clinician diagnoses, they rar...
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