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Diagnostics & Imaging · 155 stories · page 2 of 11

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Unfolding new horizons: Machine learning applications for pediatric intussusception - A systematic review and meta-analysis

Background Pediatric intussusception is among the most common surgical emergencies in early childhood. Ultrasonography is the diagnostic standard but remains operator-dependent, and no validated machine learning (ML) tools exist for triage or prognostic stratification. We systematically evaluated ML models for diagnosis and prognosis of pediatric intussusception. Methods This PROSPERO-registered systematic review and meta-analysis (CRD420251073590) included studies evaluating ML models for pediatric intussuscept…

Journal of Pediatric Surgery

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[Atrial fibrillation screening-reading the P-wave]

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Generative AI versus physicians in diagnostic radiology: a systematic review and meta-analysis

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Advancing Ultrasound Beamforming With Deep Learning: A Comprehensive Review of Methods, Datasets, Benchmarks, and Computational Challenges

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Applications and performance of imaging artificial intelligence in detecting and staging avascular necrosis of the femoral head: a systematic review and meta-analysis

Objective To evaluate the diagnostic accuracy of imaging artificial intelligence (AI) models for detecting avascular necrosis of the femoral head (AVNFH) and to examine whether diagnostic performance differs according to imaging modality, disease-stage focus, and validation strategy. Materials and methods In accordance with PRISMA-DTA, we conducted a systematic search in four main databases. Two independent reviewers evaluated the studies, extracted relevant data, and assessed the quality following the QUADAS-2…

Evidence-backed problem

Diagnostic accuracy of a DenseNet-121 deep learning algorithm for chest radiograph triage in health assessment applicants: a prospective shadow-mode validation study in Nepal

Objectives To evaluate the diagnostic accuracy of a publicly available DenseNet-121 convolutional neural network (TorchXRayVision) for triaging chest radiographs of health assessment applicants at a tertiary hospital in Nepal. Design Prospective, single-centre, shadow-mode diagnostic accuracy validation study. Reported in accordance with the Standards for Reporting of Diagnostic Accuracy Studies (STARD) 2015 checklist and STARD-Artificial Intelligence (AI)/Developmental and Exploratory Clinical Investigations of…

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Diagnostic Efficiency of Artificial Intelligence Integrated Intraoral Mobile Photographs in Identification of Oral Potentially Malignant Disorders: An Umbrella Review

Background Oral potentially malignant disorders (OPMDs) are precursors to oral squamous cell carcinoma (OSCC), a malignancy with high morbidity and mortality due to late diagnosis. Conventional diagnostic methods, though accurate, are invasive and often inaccessible in resource-limited areas. Intraoral mobile photographs integrated with artificial intelligence (AI) as a screening tool may offer a noninvasive, cost-effective, and accessible alternative for early detection of OPMDs. The study aimed to evaluate the…

Evidence-backed gain

Multiclass Diagnostic Improvement of Interstitial Cystitis (IC) and Overactive Bladder (OAB) via Machine Learning Pipeline on Surface-Enhanced Raman Spectroscopy (SERS)

Interstitial cystitis (IC) and overactive bladder (OAB) are chronic pelvic conditions with shared symptoms of urinary urgency, increased frequency, and nocturia with pain in the bladder. IC is characterized by bladder pain and inflammation without a clear etiology, whereas OAB involves detrusor overactivity in the absence of infection. Despite this symptomatic overlap, IC and OAB differ in underlying pathophysiology and require distinct treatment and medical care. A specific diagnostic strategy is currently lack…

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Melanoma Prognostication Using AI-Guided Histopathology

Accurate diagnosis and risk stratification are central for optimal management of patients with melanoma. Current American Joint Committee on Cancer (AJCC) staging systems inadequately stratify patients with clinically meaningful metastatic potential. Manual histopathologic interpretation compounds this limitation, particularly for diagnostically ambiguous lesions at the benign-malignant interface, where concordance is highly variable. This review examines how computational pathology and convolutional neural netw…

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A review on diffusion tensor imaging-based comprehensive intelligent diagnosis of Alzheimer's disease

AI-assisted early diagnosis of Alzheimer's disease (AD) has substantial clinical value, and diffusion tensor imaging (DTI), which captures white matter microstructural alterations, has considerable potential across the AD continuum. However, major barriers to clinical translation remain. This review systematically evaluated the evolution of algorithmic paradigms, the effectiveness of multimodal fusion, and barriers to clinical generalization in DTI-based AD diagnosis. Using an "Input-Model-Fusion" evaluation fra…

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Enhancing diagnostic precision for BI-RADS 4a breast nodules: A multimodal AI model integrating ultrasound radiomics, hemodynamic signatures, and clinical profiles

Background Breast Imaging Reporting and Data System (BI-RADS) 4a nodules represent a diagnostic dilemma, with a malignancy rate ranging from 2% to 10%. The majority of these nodules prove benign after biopsy, leading to unnecessary invasive procedures, patient anxiety, and healthcare costs. Current clinical practice lacks a reliable, noninvasive tool to accurately distinguish benign from malignant BI-RADS 4a lesions. Emerging evidence suggests that integrating multiparametric ultrasound data with clinical factor…

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

Background Accurate cytologic classification of thyroid nodules is essential for clinical management, but interobserver variability and indeterminate interpretations remain persistent challenges. The clinical feasibility of AIxTHY, a disease-specific deep-learning algorithm integrated into a digital cytology platform, was evaluated for assisting thyroid fine-needle aspiration (FNA) diagnosis using whole-slide imaging (WSI). Methods Two cytopathologists and one cytologist independently reviewed 100 archival ThinP…

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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…

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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…

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Imaging subtypes reveal distinct biological substrates and disability profiles in multiple sclerosis

Multiple sclerosis is characterized by marked biological heterogeneity that is only partly captured by conventional clinical phenotypes and age-at-onset categories. MRI can detect focal lesions, diffuse microstructural damage and atrophy, but these measures are usually considered separately. We therefore aimed to use Subtype and Stage Inference (SuStaIn), an unsupervised machine learning framework, to identify biologically meaningful MRI subtypes of multiple sclerosis and determine their associations with disabi…