BACKGROUND: Postoperative pulmonary complications (PPCs) are major adverse events in neurosurgical patients. This study aimed to develop and validate machine learning models predicting PPCs after neurosurgery.
OBJECTIVE: This study aimed to develop a Hashimoto's thyroiditis nodule-artificial intelligence (HTN-AI) model to optimize the diagnosis of thyroid nodules with Hashimoto's thyroiditis (HT) of which the efficiency and accuracy remain challenging.
Clinical differentiation between adolescent suicidal self-harm (SSH) and nonsuicidal self-harm (NSSH) is a significant challenge for mental health professionals, and its feasibility is controversial. The aim of the present study was to determine whet...
AIMS: Mortality risk after hospitalization for heart failure (HF) is high, especially in the first 90 days. This study aimed to construct a model automatically predicting 90 day post-discharge mortality using electronic health record (EHR) data 48 h ...
Journal of plastic, reconstructive & aesthetic surgery : JPRAS
Feb 13, 2025
BACKGROUND: In clinical practice, attaining a genuinely objective evaluation of facial aesthetics has posed considerable challenges owing to the inherent subjectivity of human observers. Artificial intelligence (AI) technology has demonstrated signif...
International journal of medical informatics
Feb 13, 2025
OBJECTIVES: While prior machine learning (ML) models for cancer survivability prediction often treated all cancer stages uniformly, cancer survivability prediction should involve understanding how different stages impact the outcomes. Additionally, t...
BACKGROUND: With widespread adoption of high-sensitivity troponin assays, more individuals with myocardial injury are now identified, with type 1 myocardial infarction (T1MI) being less common despite having the most well-established evidence base to...
RATIONALE AND OBJECTIVES: To evaluate the value of artificial intelligence (AI) assisted diagnostic system in reconstructing axial lumbar disc CT images and diagnosing lumbar disc herniation.
Deep learning (DL) for predicting Alzheimer's disease (AD) has provided timely intervention in disease progression yet still demands attentive interpretability to explain how their DL models make definitive decisions. Counterfactual reasoning has rec...
OBJECTIVE: To evaluate the performance of an automated two-step model interpreting pediatric temporomandibular joint (TMJ) magnetic resonance imaging (MRI) using artificial intelligence (AI). Using deep learning techniques, the model first automatica...
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