AIMC Topic: Retrospective Studies

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Integrating Machine Learning and Follow-Up Variables to Improve Early Detection of Hepatocellular Carcinoma in Tyrosinemia Type 1: A Multicenter Study.

International journal of molecular sciences
Hepatocellular carcinoma (HCC) is a major complication of tyrosinemia type 1 (HT-1), an inborn error of metabolism affecting tyrosine catabolism. The risk of HCC is higher in late diagnoses despite treatment. Alpha-fetoprotein (AFP) is widely used to...

Smart contours: deep learning-driven internal gross tumor volume delineation in non-small cell lung cancer using 4D CT maximum and average intensity projections.

Radiation oncology (London, England)
BACKGROUND: Delineating the internal gross tumor volume (IGTV) is crucial for the treatment of non-small cell lung cancer (NSCLC). Deep learning (DL) enables the automation of this process; however, current studies focus mainly on multiple phases of ...

Optimizing prediction of metastasis among colorectal cancer patients using machine learning technology.

BMC gastroenterology
BACKGROUND AND AIM: Colorectal cancer is among the most prevalent and deadliest cancers. Early prediction of metastasis in patients with colorectal cancer is crucial in preventing it from the advanced stages and enhancing the prognosis among these pa...

Daily Automated Prediction of Delirium Risk in Hospitalized Patients: Model Development and Validation.

JMIR medical informatics
BACKGROUND: Delirium is common in hospitalized patients and is correlated with increased morbidity and mortality. Despite this, delirium is underdiagnosed, and many institutions do not have sufficient resources to consistently apply effective screeni...

Machine learning based radiomic models outperform clinical biomarkers in predicting outcomes after immunotherapy for hepatocellular carcinoma.

Journal of hepatology
BACKGROUND & AIMS: Atezolizumab plus bevacizumab (A/B) is a first-line therapy for unresectable hepatocellular carcinoma (HCC). Only a small proportion of patients respond to treatment. This study integrated radiomic and clinical data derived from ro...

Combined peritumoral radiomics and clinical features predict 12-month progression free survival in glioblastoma.

Journal of neuro-oncology
PURPOSE: Analyzing post-treatment MRIs from glioblastoma patients can be challenging due to similar radiological presentations of disease progression and treatment effects. Identifying radiomics features (RFs) revealing progressive glioblastoma can c...

Development and Validation an AI Model to Improve the Diagnosis of Deep Infiltrating Endometriosis for Junior Sonologists.

Ultrasound in medicine & biology
OBJECTIVE: This study aims to develop and validate an artificial intelligence (AI) model based on ultrasound (US) videos and images to improve the performance of junior sonologists in detecting deep infiltrating endometriosis (DE).

A machine learning-based framework for predicting postpartum chronic pain: a retrospective study.

BMC medical informatics and decision making
BACKGROUND: Postpartum chronic pain is prevalent, affecting many women after delivery. Machine learning algorithms have been widely used in predicting postoperative conditions. We investigated the prevalence of and risk factors for postpartum chronic...

Automated machine learning for early prediction of systemic inflammatory response syndrome in acute pancreatitis.

BMC medical informatics and decision making
BACKGROUND: Systemic inflammatory response syndrome (SIRS) is a frequent and serious complication of acute pancreatitis (AP), often associated with increased mortality. This study aims to leverage automated machine learning (AutoML) algorithms to cre...