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HealthEvidence-backed gain

Comparative evaluation of VoxelMorpand conventional deformable image registration algorithms for thoracic 4D-CT in radiotherapy

Background Deformable image registration (DIR) is essential for thoracic four-dimensional computed tomography (4D-CT)-based radiotherapy applications. Recently, deep learning-based DIR methods such as VoxelMorph have been proposed; however, their performance relative to clinically used DIR algorithms remains unclear. Purpose This study aimed to evaluate the DIR accuracy of VoxelMorph for thoracic 4D-CT and to compare it with conventional clinical and research-oriented DIR methods. Materials and methods Thoracic…

Journal of Applied Clinical Medical Physics

Evidence-backed problem

Artificial Intelligence-Based Chatbots in Genetic Counseling Practice: Current Uptake, Utilization, and Perspectives

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Artificial intelligence-assisted digital thyroid FNA cytology: Improved agreement and sensitivity for higher-risk Bethesda categories with enhanced screening efficiency

Evidence-backed gain

A Machine Learning-Derived Risk Scorecard for Pneumonia Hospitalization in Japanese Old-Old Adults

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Incremental Propensity Score Interventions: A Primer for Pharmacoepidemiologists

Background In observational pharmacoepidemiology, estimating average treatment effects (ATEs) is often challenging due to a lack of practical positivity. In highly selective clinical settings, certain patients almost always or never receive treatment, causing ATE estimators to rely on unstable extrapolation. Incremental propensity score interventions (IPSIs) offer a stochastic alternative by shifting each patient's probability of treatment, providing a more clinically realistic framework that circumvents positiv…

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Comparison of the Cox Proportional Hazards Model and Random Survival Forest Algorithm for Predicting Patient-Specific Survival Probabilities in Clinical Trial Data

The Cox proportional hazards model is often used to analyze data from randomized controlled trials (RCTs) with time-to-event outcomes. Random survival forest (RSF) is a machine-learning algorithm known for its high predictive performance. We conduct a comprehensive neutral comparison study to compare the performance of Cox regression and RSF in various simulation scenarios based on two reference datasets from RCTs. The motivation is to identify settings in which one method is preferable over the other when compa…

Evidence-backed gain

Identification and Experimental Validation of PANoptosis Key Genes for Constructing a PANoptosis Risk Diagnostic Model in Intervertebral Disc Degeneration

Background Intervertebral disc degeneration (IDD) represents a significant health concern globally. This study aimed to identify the PANoptosis key genes (PKGs) associated with IDD and construct a risk diagnosis model METHODS: Single-cell sequencing, machine learning algorithms, and LASSO regression were employed to identify PKGs and develop a risk model. The expression of identified PKGs was validated in human IDD clinical tissues using qRT-PCR, Western blot, and immunohistochemistry. The therapeutic effect of…

Evidence-backed gain

Profluent And BioMarin Pharmaceutical Inc. Partner To Design Novel Medicines Using AI

Profluent announced a collaboration with BioMarin Pharmaceutical Inc. to design and optimize novel enzyme therapies for BioMarin's pipeline. Under the agreement, Profluent will apply its foundational...

Health

Integrated Multi-Omics Reveals Cellular States and Microenvironmental Remodeling in Coexisting DCIS and IDC

Ductal carcinoma in situ (DCIS) is a non-invasive precursor of invasive ductal carcinoma (IDC), yet the biological mechanisms underlying the transition from DCIS to IDC remain incompletely understood. Here, we integrate spatial transcriptomics, single-cell RNA sequencing, and single-cell DNA sequencing on coexisting DCIS and IDC samples to characterize cellular and microenvironmental alterations. Integrated analyses reveal differential molecular characterizations between coexisting DCIS and IDC and identify cand…

Health

Machine learning-based discrimination of metabolic dysfunction-associated steatotic liver disease: implications of tryptophan metabolites as potential biomarkers

Objectives Early screening of individuals at high risk of metabolic dysfunction-associated steatotic liver disease (MASLD) facilitates timely intervention and may reduce the risk of progression to cirrhosis. This study aimed to develop a reliable and clinically practical model for MASLD discrimination. Methods A total of 179 individuals were recruited, including 80 healthy controls and 99 MASLD patients. Non-targeted and targeted metabolomics were performed to identify and validate candidate biomarkers associate…

Health

Evaluation of the detection accuracy of a fully automated AI-based lesion segmentation tool in whole-body FDG PET/CT for lymphoma

Background This study evaluated an AI-based lesion segmentation tool using a hybrid 2D-3D deep learning model based on the nnUNet architecture with a ResNet18 backbone, incorporating post-processing to reduce false positives and segmentation-related quantitative errors. Methods Whole-body FDG PET/CT scans from 72 lymphoma patients (mean age 39.2 ± 18.6 years; 33 females, 39 males; 43.1% Hodgkin lymphoma, 56.9% non-Hodgkin lymphoma) acquired on a uMI550 digital PET/CT system were retrospectively analysed. Automat…

Health

Machine learning-based nomogram for acute kidney injury after liver transplantation

Acute kidney injury (AKI) after liver transplantation (LT) is a frequent complication associated with adverse outcomes, yet practical early postoperative prediction tools remain limited. This retrospective study developed and internally validated an interpretable model for AKI after LT using perioperative variables available by the time of the first urgent postoperative blood test, generally obtained within 1 h after surgery. Adult LT recipients treated from June 2023 to January 2026 were screened. AKI was defin…

Health

Cardiovascular Classification in Middle-Aged and Elderly CKD Patients: A Machine Learning Approach in China and the United States

Background Cardiovascular disease (CVD) drives mortality in chronic kidney disease (CKD) patients. Conventional risk tools underperform in CKD, and few machine learning (ML) models undergo cross-national validation. We developed interpretable ML models for classifying CVD amongst Chinese and US middle-aged and elderly CKD patients. Methods We conducted retrospective cross-sectional analyses using two nationally representative cohorts: NHANES (2011-2016, US) and CHARLS (Wave 1 and 3, China). A total of 1057 CKD p…

Health

Deep Learning and Machine Learning Algorithms for Cervical Cancer Segmentation on MRI: A Systematic Review

Introduction Cervical cancer management relies heavily on magnetic resonance imaging (MRI) for staging and treatment planning; however, manual segmentation is time-consuming and prone to variability. This systematic review aimed to evaluate the performance and clinical potential of machine learning (ML) and deep learning (DL) algorithms for automated cervical cancer segmentation on MRI. Methods A systematic review guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (PROSPERO…

Health

Exploring the Potential of Parent Report for Autism/Developmental Screening at the 18-Month Visit

Purpose To develop optimal algorithms of parent-administered items to improve the detection of autism and other developmental disorders at the 18-month visit. Methods Parents of 11,878 children aged 16-20 months completed the M-CHAT-R/F ™ , Q-CHAT-10-O, and ASQ-3 R at scheduled 18-month pediatric visits via an online system. Ninety-six children with positive screens and 314 matched controls completed additional items, including the POSI; items from the FYI and POEM data banks, and the MacArthur-Bates Communicati…