AIMC Topic: Retrospective Studies

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Multimodal Deep Learning for Grading Carpal Tunnel Syndrome: A Multicenter Study in China.

Academic radiology
RATIONALE AND OBJECTIVES: Ultrasound (US)-based deep learning (DL) models for grading the severity of carpal tunnel syndrome (CTS) are scarce. We aimed to advance CTS grading by developing a joint-DL model integrating clinical information and multimo...

Deep Learning Based on Ultrasound Images Differentiates Parotid Gland Pleomorphic Adenomas and Warthin Tumors.

Ultrasonic imaging
Exploring the clinical significance of employing deep learning methodologies on ultrasound images for the development of an automated model to accurately identify pleomorphic adenomas and Warthin tumors in salivary glands. A retrospective study was c...

Machine learning-based risk prediction model for arteriovenous fistula stenosis.

European journal of medical research
BACKGROUND: Arteriovenous fistula stenosis is a common complication in hemodialysis patients, yet effective predictive tools are lacking. This study aims to develop an interpretable machine learning model for stenosis risk prediction.

Deep learning-based prediction of cervical canal stenosis from mid-sagittal T2-weighted MRI.

Skeletal radiology
OBJECTIVE: This study aims to establish a large degenerative cervical myelopathy cohort and develop deep learning models for predicting cervical canal stenosis from sagittal T2-weighted MRI.

International external validation of the SORG machine learning algorithm for predicting sustained postoperative opioid prescription after anterior cervical discectomy and fusion using a Taiwanese cohort of 1,037 patients.

The spine journal : official journal of the North American Spine Society
BACKGROUND CONTEXT: Anterior cervical discectomy and fusion (ACDF) is widely performed for cervical spine disorders, with opioids commonly prescribed postoperatively for pain management. However, prolonged opioid use carries significant risks such as...

Prognostic value of SAPS II score for 28-day mortality in ICU patients with acute pulmonary embolism.

International journal of cardiology
BACKGROUND: Acute pulmonary embolism (APE) is a common and life-threatening emergency in intensive care units (ICUs). Effective risk assessment tools are essential to improve patient outcomes. This study aims to evaluate the association between Simpl...

Exploring the Incremental Value of Aorta Enhancement Normalization Method in Evaluating Renal Cell Carcinoma Histological Subtypes: A Multi-center Large Cohort Study.

Academic radiology
RATIONALE AND OBJECTIVES: The classification of renal cell carcinoma (RCC) histological subtypes plays a crucial role in clinical diagnosis. However, traditional image normalization methods often struggle with discrepancies arising from differences i...

Machine Learning-Assisted Health Economics and Policy Reviews: A Comparative Assessment.

Applied health economics and health policy
INTRODUCTION: The growth of scientific literature in health economics and policy represents a challenge for researchers conducting literature reviews. This study explores the adoption of a machine learning (ML) tool to enhance title and abstract scre...

Artificial intelligence-based multimodal prediction for nuclear grading status and prognosis of clear cell renal cell carcinoma: a multicenter cohort study.

International journal of surgery (London, England)
BACKGROUND: The assessment of the International Society of Urological Pathology (ISUP) nuclear grade is crucial for the management and treatment of clear cell renal cell carcinoma (ccRCC). This study aimed to explore the value of using integrated mul...

Developing a high-performance AI model for spontaneous intracerebral hemorrhage mortality prediction using machine learning in ICU settings.

BMC medical informatics and decision making
BACKGROUND: Spontaneous intracerebral hemorrhage (SICH) is a devastating condition that significantly contributes to high mortality rates. This study aims to construct a mortality prediction model for patients with SICH using four various artificial ...