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All Diagnostics & Imaging Drug Discovery Mental Health Hospitals & Care

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Robotic Ultrasound Imaging: A Comprehensive Review of Historical Evolution, Current State-of-the-Art, and Future Perspectives

Ultrasound imaging is an indispensable diagnostic tool, yet its profound reliance on operator expertise inherently restricts its reproducibility and global accessibility. Robotic ultrasound systems (RUSS) have evolved over the past 2 decades to mitigate these limitations by mechanically decoupling the human operator from the patient. This comprehensive review examines the historical trajectory of medical ultrasonography and robotics, highlighting their convergence into modern RUSS. We detail the taxonomies of ro…

Journal of Ultrasound in Medicine · Health

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Diagnosing melioidosis and tracking treatment outcomes using breath

Melioidosis is a life-threatening infectious disease caused by Burkholderia pseudomallei ( Bp ). Rapid diagnosis and appropriate antimicrobial treatment are critical to reduce mortality, yet diagnosis is hindered by diverse clinical manifestations, mimicry with other diseases, and reliance on slow culture-based methods. Detecting volatile compounds offers a non-invasive approach for rapid infection detection. In this study, we aim to identify volatile compounds in patients' breath that can aid in diagnosing meli…

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Evidence-backed problem

From accuracy to service: deciding what artificial intelligence outputs may do in veterinary diagnostic laboratories

Artificial intelligence (AI) tools are entering veterinary diagnostic laboratory service, but reported model accuracy does not determine what the laboratory staff should allow an output to do. This Commentary defines service entry as the point at which an AI output is allowed to influence case triage, interpretation, a draft report, or result release. Before that point, the laboratory staff should first decide whether the submitted specimen can support the question being asked. They should then document 7 decisi…

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An Umbrella Review of Artificial Intelligence Applications in Mental Health Care

Background Artificial intelligence (AI) is increasingly used in mental health care to address rising demand, workforce shortages and access barriers; however, evidence remains scattered across multiple systematic reviews, limiting synthesis and practical application. Objective To synthesise evidence on AI applications in mental health care, including trends, uses, benefits, challenges and risk-mitigation strategies, guided by an ethics of care framework. Methods This umbrella review of systematic reviews followe…

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Evidence-backed gain

Integrative single-cell and machine-learning analysis identifies LGALS1 as a macrophage-associated diagnostic and prognostic biomarker in hepatocellular carcinoma

Background Hepatocellular carcinoma (HCC) is characterized by marked heterogeneity and an immunosuppressive microenvironment in which tumor-associated macrophages contribute to disease progression. This study aimed to identify macrophage-associated biomarkers with diagnostic, prognostic, and translational relevance in HCC. Methods Single-cell RNA-sequencing datasets were integrated with bulk transcriptomic and clinical data from TCGA-LIHC, GEO, and ICGC cohorts. Macrophage markers were intersected with tumor-ass…

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$Systematic assessment of the medical utility of radiology and diagnostic Artificial Intelligence in fracture detection (SAMURAI-fracture): a protocol for a multicentre cluster-randomised controlled trial$

Evidence-backed problem

Systematic assessment of the medical utility of radiology and diagnostic Artificial Intelligence in fracture detection (SAMURAI-fracture): a protocol for a multicentre cluster-randomised controlled trial

Fracture misdiagnosis is a common diagnostic error in emergency departments (EDs) and minor injury units (MIUs), leading to poor patient outcomes, unnecessary treatments and significant healthcare costs. Artificial intelligence (AI)-assisted fracture detection tools are now available for use in radiology workflows; however, the impact of these technologies on patient outcomes, experiences and overall care pathways in the real-world clinical setting is limited. We will conduct a prospective cluster randomised cro…

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Evidence-backed problem

Artificial Intelligence-Assisted Chest Radiography: A Prospective Crossover Multi-Reader Study on Diagnostic Performance and Workflow Efficiency

To evaluate the impact of four commercially available AI solutions for chest radiography on diagnostic performance, workflow efficiency, and clinical decision-making in a real-world setting. In this prospective, monocentric, crossover reader study, five readers (one to six years of experience) assessed 1200 consecutive patients undergoing chest radiography (1861 total radiographs) for pulmonary infiltrates, pleural effusions, mediastinal masses, pneumothorax, and pulmonary nodules under five conditions: without…

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Radiological assessment of pulmonary vascularity in congenital heart disease: standardized clinician assessment versus deep learning-based prediction

Background Assessment of pulmonary vascularity on chest radiographs (CXRs) in congenital heart disease (CHD) is limited by subjectivity, and existing criteria lack sufficient validation. Artificial intelligence-based deep learning model (DLM) analysis may improve accuracy. We developed and validated structured criteria and a DLM to evaluate pulmonary vascularity and compared it with conventional clinical interpretation. Methods A total of 400 deidentified CXRs from CHD patients with Fick-derived pulmonary-to-sys…

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Radiological assessment of pulmonary vascularity in congenital heart disease: standardized clinician assessment versus deep learning-based prediction

Explainable artificial intelligence in medical imaging: how to interpret, evaluate, and use artificial intelligence explanations

Most artificial intelligence (AI) models used in radiology are black boxes-they produce predictions without explaining the basis of their outputs, raising concerns about clinical safety, accountability, and trust. To address this, a growing body of methods has been developed to help clinicians understand and evaluate AI predictions. This field, known as explainable AI (XAI), aims to help clinicians interrogate, interpret, and critically evaluate AI predictions by identifying factors associated with model outputs…

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Evaluation of Differential Diagnosis of Odontogenic Lesions in Cone Beam Computed Tomography Images Using Radiomics-Based Machine Learning

Objective This study aimed to evaluate the structural characteristics of mandibular alveolar bone in patients with Type 1 diabetes mellitus (T1DM), Type 2 diabetes mellitus (T2DM), and systemically healthy controls using panoramic radiography-based radiomic analysis combined with machine learning algorithms. Materials and methods A total of 225 panoramic radiographs (75 T1DM, 75 T2DM, 75 healthy controls) were retrospectively analyzed. ROIs were segmented from eight anatomical mandibular segments per subject, an…

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Evaluation of gastric cancer using artificial intelligence iterative reconstruction on abdominal CT: image quality and diagnostic accuracy

To investigate the clinical performance of a novel deep-learning based image reconstruction algorithm, namely artificial intelligence iterative reconstruction (AIIR), for assessing gastric cancer (GC) on CT. We prospectively enrolled 132 GC patients without history of treatment, all of whom subsequently underwent surgical resection or staging laparoscopy. All patients underwent preoperative abdominal contrast-enhanced CT examinations, and images were reconstructed with both hybrid iterative reconstruction (HIR)…

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A multimodal MRI-based deep learning model for non-invasive diagnosis of pineal region germinoma

PURPOSE: To develop a deep learning model for the preoperative, non-invasive identification of germinomas in the pineal region. This retrospective study included 114 patients with pathologically confirmed pineal region tumors. The cohort was randomly divided into a training set (n = 91) and a test set (n = 23). The training set was further partitioned into three folds for cross-validation. A convolutional neural network (CNN) enhanced with contrastive learning was used to extract discriminative features from ind…

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Automated diagnostic system for classification of progression stages of osteoarthritis using magnetic resonance imaging

Osteoarthritis (OA) is a degenerative joint disease characterized by cartilage loss, synovial fluid imbalance, and bone structural changes, leading to reduced mobility. Most clinical studies use MRI-derived cartilage characteristics to assess OA progression. To support timely treatment decisions and minimize human error, an automated computer aided system is needed for prediction of OA in the progressive stages. To build the automatic system for classifying progression phases of OA, we present a novel hybrid fra…

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An Introduction to the Machine Learning Lifecycle for Clinical Microbiology

Clinical microbiology is undergoing rapid transformation driven by modern technologies generating high-volume, high-dimensional, and heterogeneous datasets that exceed the analytical capabilities of traditional rule-based approaches. Artificial intelligence (AI) provides powerful computational methods to gain diagnostic, biological, and epidemiological insights from these complex data. This narrative review synthesizes information from peer-reviewed literature in clinical microbiology and machine learning, inclu…

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An Introduction to the Machine Learning Lifecycle for Clinical Microbiology

A Combined Deep Learning Approach to Screen Patients for Neuromuscular Pathology

Neuromuscular diseases (NMD), comprising over 600 different conditions, severely impact nerve and/or muscle function and lead to significant morbidity. Ultrasound is a non-invasive tool that is gaining acceptance for diagnosing NMD. In clinical practice, muscle ultrasound can be evaluated quantitatively or visually using an ordinal four-point grading score (Heckmatt score). Its current application is limited by time investment in manual analysis and lack of result transferability to other centers. Here, we prese…

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A Combined Deep Learning Approach to Screen Patients for Neuromuscular Pathology