AIMC Topic: Retrospective Studies

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UNIK (Urologic Non-Neoplastic Investigation of Kidneys): a machine learning approach to decode benign lesion.

World journal of urology
PURPOSE: Predicting the likelihood of benign neoplasia in patients with suspected renal cell carcinoma (RCC) is a cornerstone of presurgical planning. We sought to create and validate U.N.I.K., a machine learning (ML) model capable of predicting beni...

Machine learning models for predicting severe acute kidney injury in patients with sepsis-induced myocardial injury.

Scientific reports
Severe acute kidney injury (sAKI) is a prevalent and serious complication among patients with sepsis-induced myocardial injury (SIMI). Prompt and early prediction of sAKI has an important role in timely intervention, ultimately improving the patients...

Development of a machine learning-based model to predict urethral recurrence following radical cystectomy: a multicentre retrospective study and updated meta-analysis.

Scientific reports
Urethral recurrence (UR) following radical cystectomy for bladder cancer represents an aggressive disease failure with typically poor survival outcomes. Our study aimed to assess the predictive risk factors for UR, to develop and validate an easy-to-...

Deep learning model for differentiating thyroid eye disease and orbital myositis on computed tomography (CT) imaging.

Orbit (Amsterdam, Netherlands)
PURPOSE: To develop a deep learning model using orbital computed tomography (CT) imaging to accurately distinguish thyroid eye disease (TED) and orbital myositis, two conditions with overlapping clinical presentations.

Effect of contrast enhancement on diagnosis of interstitial lung abnormality in automatic quantitative CT measurement.

European radiology
OBJECTIVE: To investigate the effect of contrast enhancement on the diagnosis of interstitial lung abnormalities (ILA) in automatic quantitative CT measurement in patients with paired pre- and post-contrast scans.

Artificial intelligence vs human expertise: A comparison of plantar fascia thickness measurements through MRI imaging.

International journal of medical informatics
OBJECTIVE: This study aims to evaluate the reliability of plantar fascia thickness measurements performed by ChatGPT-4 using magnetic resonance imaging (MRI) compared to those obtained by an experienced clinician.

Explainable AI assisted vertebral refracture diagnosis after percutaneous vertebroplasty through effective feature engineering and stacked ensemble learning.

International journal of medical informatics
BACKGROUND AND OBJECTIVE: To develop and validate a machine learning model based on stacking ensemble learning and feature selection strategies to predict vertebral refracture risk after percutaneous vertebroplasty.

Developing a CT radiomics-based model for assessing split renal function using machine learning.

Japanese journal of radiology
PURPOSE: This study aims to investigate whether non-contrast computed tomography radiomics can effectively reflect split renal function and to develop a radiomics model for its assessment.

Uncovering nonlinear patterns in time-sensitive prehospital breathing emergencies: an exploratory machine learning study.

BMC medical informatics and decision making
BACKGROUND: Timely prehospital care is crucial for patients presenting with high-risk time-sensitive (HRTS) conditions. However, the interplay between response time and demographic factors in patients with breathing problems remains insufficiently un...