AIMC Topic: Retrospective Studies

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Deep learning-driven modality imputation and subregion segmentation to enhance high-grade glioma grading.

BMC medical informatics and decision making
PURPOSE: This study aims to develop a deep learning framework that leverages modality imputation and subregion segmentation to improve grading accuracy in high-grade gliomas.

Intelligent Prediction Platform for Sepsis Risk Based on Real-Time Dynamic Temporal Features: Design Study.

JMIR medical informatics
BACKGROUND: The development of sepsis in the intensive care unit (ICU) is rapid, the golden rescue time is short, and the effective way to reduce mortality is rapid diagnosis and early warning. Therefore, real-time prediction models play a key role i...

Three-dimensional automated segmentation of adolescent idiopathic scoliosis on computed tomography driven by deep learning: A retrospective study.

Medicine
Accurate vertebrae segmentation is crucial for modern surgical technologies, and deep learning networks provide valuable tools for this task. This study explores the application of advanced deep learning-based methods for segmenting vertebrae in comp...

CCTA-Derived coronary plaque burden offers enhanced prognostic value over CAC scoring in suspected CAD patients.

European heart journal. Cardiovascular Imaging
AIMS: To assess the prognostic utility of coronary artery calcium (CAC) scoring and coronary computed tomography angiography (CCTA)-derived quantitative plaque metrics for predicting adverse cardiovascular outcomes.

Prediction of clinical stages of cervical cancer via machine learning integrated with clinical features and ultrasound-based radiomics.

Scientific reports
To investigate the prediction of a model constructed by combining machine learning (ML) with clinical features and ultrasound radiomics in the clinical staging of cervical cancer. General clinical and ultrasound data of 227 patients with cervical can...

Deep learning radiomics fusion model to predict visceral pleural invasion of clinical stage IA lung adenocarcinoma: a multicenter study.

Journal of cardiothoracic surgery
AIM: To assess the predictive performance, risk stratification capabilities, and auxiliary diagnostic utility of radiomics, deep learning, and fusion models in identifying visceral pleural invasion (VPI) in lung adenocarcinoma.

Real-time segmentation and detection of ponticulus posticus in lateral cephalometric radiographs using YOLOv8: a step towards enhanced clinical evaluation.

BMC oral health
OBJECTIVES: Ponticulus posticus (PP) is a bony structure in the cervical spine, often difficult to identify in radiographic images, and its detection is important for both orthodontic diagnosis and clinical decision-making related to craniovertebral ...

Clinical and economic effectiveness of Schroth therapy in adolescent idiopathic scoliosis: insights from a machine learning- and active learning-based real-world study.

Journal of orthopaedic surgery and research
BACKGROUND: Adolescent idiopathic scoliosis (AIS) is a prevalent musculoskeletal condition affecting approximately 2-3% of the adolescent population. Although exercise-based therapeutic interventions are increasingly employed as non-surgical alternat...

Machine learning decision support model construction for craniotomy approach of pineal region tumors based on MRI images.

BMC medical imaging
BACKGROUND: Pineal region tumors (PRTs) are rare but deep-seated brain tumors, and complete surgical resection is crucial for effective tumor treatment. The choice of surgical approach is often challenging due to the low incidence and deep location. ...