AIMC Topic: Retrospective Studies

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Precision identification of endometrial malignancy and precancerous lesions: Development of a machine learning model incorporating multidimensional clinical and imaging parameters.

Medicine
To develop and validate a machine learning (ML) model integrating multidimensional clinical, pathomic, and ultrasound radiomic parameters for precise identification of endometrial malignancy and precancerous lesions, with a focus on addressing the di...

P22 Using VECTRA and AI analysis to monitor paediatric lesions: a review of cases.

The British journal of dermatology
BACKGROUND: Paediatric melanoma is a rare but important diagnosis. In the paediatric cohort, diagnostic challenges arise due to lesion variability and the inherent difficulties associated with paediatric assessment. Clinical decision-making is furthe...

[Identification of high-risk preoperative blood indicators and baseline characteristics for multiple postoperative complications in rheumatoid arthritis patients undergoing total knee arthroplasty: a multi-machine learning feature contribution analysis].

Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery
OBJECTIVE: To explore, identify, and develop novel blood-based indicators using machine learning algorithms for accurate preoperative assessment and effective prediction of postoperative complication risks in patients with rheumatoid arthritis (RA) u...

Machine learning-driven prediction of readmission risk in heart failure patients with diabetes: synergistic assessment of inflammatory and metabolic biomarkers.

International journal of cardiology
BACKGROUND: Heart failure (HF) and diabetes mellitus (DM) frequently coexist, exacerbating disease progression and increasing hospital readmission risk. Accurate prediction of readmission in HF patients with DM remains a clinical challenge. This stud...

Comparative effectiveness of anti-seizure medications in emulated trials using medical informatics.

Brain : a journal of neurology
Anti-seizure medications (ASMs) are often prescribed using a trial-and-error approach with a similar sequence for many patients. Comparative effectiveness data beyond the first ASM prescription are limited. Artificial intelligence can automatically e...

Photon-Counting Detector CT of the Brain Reduces Variability of Hounsfield Units and Has a Mean Offset Compared with Energy-Integrating Detector CT.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Distinguishing GM from WM is essential for CT of the brain. The recently established photon-counting detector (PCD)-CT technology uses a novel detection technique that might allow more precise measurement of tissue attenuation...

Deep Learning-Based Prediction of PET Amyloid Status Using MRI.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Identifying amyloid-beta (Aβ)-positive patients is essential for Alzheimer disease clinical trials and disease-modifying treatments but currently requires PET or CSF sampling. Previous MRI-based deep learning models using only...