AIMC Topic: Predictive Value of Tests

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A machine learning approach using stone volume to predict stone-free status at ureteroscopy.

World journal of urology
INTRODUCTION: To develop a predictive model incorporating stone volume along with other clinical and radiological factors to predict stone-free (SF) status at ureteroscopy (URS).

Cherry on Top or Real Need? A Review of Explainable Machine Learning in Kidney Transplantation.

Transplantation
Research on solid organ transplantation has taken advantage of the substantial acquisition of medical data and the use of artificial intelligence (AI) and machine learning (ML) to answer diagnostic, prognostic, and therapeutic questions for many year...

Prediction of retinopathy progression using deep learning on retinal images within the Scottish screening programme.

The British journal of ophthalmology
BACKGROUND/AIMS: National guidelines of many countries set screening intervals for diabetic retinopathy (DR) based on grading of the last screening retinal images. We explore the potential of deep learning (DL) on images to predict progression to ref...

Deep learning and digital pathology powers prediction of HCC development in steatotic liver disease.

Hepatology (Baltimore, Md.)
BACKGROUND AND AIMS: Identifying patients with steatotic liver disease who are at a high risk of developing HCC remains challenging. We present a deep learning (DL) model to predict HCC development using hematoxylin and eosin-stained whole-slide imag...

Machine learning prediction of one-year mortality after percutaneous coronary intervention in acute coronary syndrome patients.

International journal of cardiology
BACKGROUND: Machine learning (ML) models have the potential to accurately predict outcomes and offer novel insights into inter-variable correlations. In this study, we aimed to design ML models for the prediction of 1-year mortality after percutaneou...

Computed tomography machine learning classifier correlates with mortality in interstitial lung disease.

Respiratory investigation
BACKGROUND: A machine learning classifier system, Fibresolve, was designed and validated as an adjunct to non-invasive diagnosis in idiopathic pulmonary fibrosis (IPF). The system uses a deep learning algorithm to analyze chest computed tomography (C...

Impact of an artificial intelligence based model to predict non-transplantable recurrence among patients with hepatocellular carcinoma.

HPB : the official journal of the International Hepato Pancreato Biliary Association
OBJECTIVE: We sought to develop Artificial Intelligence (AI) based models to predict non-transplantable recurrence (NTR) of hepatocellular carcinoma (HCC) following hepatic resection (HR).

New Diagnostic Tools for Pulmonary Embolism Detection.

Methodist DeBakey cardiovascular journal
The presentation of pulmonary embolism (PE) varies from asymptomatic to life-threatening, and management involves multiple specialists. Timely diagnosis of PE is based on clinical presentation, D-dimer testing, and computed tomography pulmonary angio...

Deep Learning Model for Predicting Proliferative Hepatocellular Carcinoma Using Dynamic Contrast-Enhanced MRI: Implications for Early Recurrence Prediction Following Radical Resection.

Academic radiology
RATIONALE AND OBJECTIVES: The proliferative nature of hepatocellular carcinoma (HCC) is closely related to early recurrence following radical resection. This study develops and validates a deep learning (DL) prediction model to distinguish between pr...