Artificial Intelligence Medical Compendium

Glioblastoma (GBM) treatment response is highly heterogeneous. Predicting outcomes from pre-treatment MRI is a critical unmet need, yet the clinical adoption of "black box" deep learning (DL) models is hindered by a lack of transparency. Robust and h... read more 

Accurate radiographic classification of hip fractures into femoral neck (intracapsular) versus intertrochanteric (extracapsular) types is essential for surgical planning, yet most AI studies focus on binary detection using large datasets from high-re... read more 

The translation of deep learning architectures from controlled experimental settings into real-world cancer diagnostics is heavily bottlenecked by domain shift and demographic bias. While models routinely achieve near-perfect retrospective performanc... read more 

This study validates a deep learning model for automated ureteral length measurement on CT using gross specimen measurements as the reference standard. A deep learning model was trained on 3629 CT series from 1502 patients (cohort 1) to segment the u... read more 

Preoperative molecular subtyping of breast cancer is essential for treatment selection and prognosis assessment, yet most deep learning approaches formulate subtype prediction as a conventional flat multi-class task without reflecting the sequential ... read more 

Postoperative outcomes following lumbar spine surgery remain variable, and existing clinical risk stratification methods offer limited predictive accuracy. Advances in machine learning enable quantitative analysis of routinely acquired spinal imaging... read more 

Knee osteoarthritis (OA) causes symptoms such as pain, stiffness, and functional limitation in the knees; plain knee radiographs are used in addition to physical examination in its diagnosis. This study aimed to compare the performance of pre-trained... read more 

Despite advances in computed tomography (CT)-based classification systems for posterior malleolar fractures (PMFs), diagnostic consistency remains limited because these systems are still highly dependent on the observer's experience. This study aimed... read more 

This retrospective two-center diagnostic study was aimed at developing and externally testing an interpretable MRI-based machine learning model for differentiating endometrial stromal sarcoma (ESS) from cellular leiomyoma (CL). A total of 155 women w... read more 

Histopathology-based cancer diagnosis is crucial for early detection and timely therapeutic intervention. However, most deep learning methods for histopathological image classification are mainly optimized as prediction models, with limited considera... read more