Osteoporosis is an under-screened musculoskeletal disorder that results in diminished quality of life and significant burden to the healthcare system. We aimed to evaluate the ability of Rho, an artificial intelligence (AI) tool, to prospectively identify patients at-risk for low bone mineral density (BMD) from standard x-rays, its adoption rate by radiologists, and acceptance by primary care providers (PCPs). Patients ≥50 years were recruited when undergoing an x-ray of a Rho-eligible body part for any clinical indication. Questionnaires were completed at baseline and 6-month follow-up, and PCPs of "Rho-Positive" patients (those likely to have low BMD) were asked for feedback. Positive predictive value (PPV) was calculated in patients who returned within 6 months for a DXA. Of 1145 patients consented, 987 had x-rays screened by Rho, and 655 were flagged as Rho-Positive. Radiologists included this finding in 524 (80%) of reports. Of all Rho-Positive patients, 125 had a DXA within 6 months; Rho had a 74% PPV for DXA T-Score <-1. From 51 PCP responses, 78% found Rho beneficial. Of 389 patients with follow-up questionnaire data, a greater proportion of Rho-Positive versus -negative patients had discussed bone health with their PCP since study start (36% vs 18%, <i>P</i> < .001), or were newly diagnosed with osteoporosis (11% vs 5%; <i>P</i> = .03). By identifying patients at-risk of low BMD, with acceptability of reporting by radiologists and generally positive feedback from PCPs, Rho has the potential to improve low screening rates for osteoporosis by leveraging existing x-ray data.

To develop and evaluate the clinical feasibility of a dynamic computed tomography (CT) protocol for assessing patellofemoral (PF) and tibiofemoral (TF) kinematics before and after total knee arthroplasty (TKA), and to quantify postoperative kinematic changes in a pilot study. In this prospective single-centre study, patients with primary osteoarthritis scheduled for cemented TKA underwent dynamic CT scans preoperatively and at 1-year follow-up during active flexion-extension-flexion. Preoperatively, the femur, tibia and patella were segmented using a neural network. Postoperatively, computer-aided design (CAD) implant models were aligned to CT data to determine relative implant-bone orientation. Due to metal artefacts, preoperative patella meshes were manually aligned to postoperative scans by four raters, and averaged for analysis. Anatomical coordinate systems were applied to quantify patellar flexion, tilt, proximal tip rotation, mediolateral translation and femoral condyle anterior-posterior translation. Descriptive statistics were reported, and interoperator agreement for patellar registration was assessed using intraclass correlation coefficients (ICCs). Ten patients (mean age, 65 ± 8 years; 6 men) were analysed across a shared flexion range of 14°-55°. Postoperatively, the patella showed increased flexion (median difference: 0.9°-3.9°), medial proximal tip rotation (median difference: 1.5°-6.0°), lateral tilt (median difference: 2.7°-5.5°), and lateral shift (median difference: -1.5 to -2.8 mm). The medial and lateral femoral condyles translated 2-4 mm anterior-posteriorly during knee flexion. Interoperator agreement for patellar registration ranged from good to excellent across all parameters (ICC = 0.85-1.00). This pilot study demonstrates that dynamic CT enables in vivo assessment of PF and TF kinematics before and after TKA. The protocol quantified postoperative kinematic changes and demonstrated potential as research tool. Further automation is needed to investigate relationships between these kinematic patterns and patient outcomes in larger-scale studies. Level III.

Given the high burden of hepatocellular carcinoma (HCC), risk stratification in patients with cirrhosis is critical but remains inadequate. In this study, we aimed to develop and validate an HCC prediction model by integrating radiomics and deep learning features from liver and spleen computed tomography (CT) images into the established age-male-ALBI-platelet (aMAP) clinical model. Patients were enrolled between 2018 and 2023 from a Chinese multicenter, prospective, observational cirrhosis cohort, all of whom underwent 3-phase contrast-enhanced abdominal CT scans at enrollment. The aMAP clinical score was calculated, and radiomic (PyRadiomics) and deep learning (ResNet-18) features were extracted from liver and spleen regions of interest. Feature selection was performed using the least absolute shrinkage and selection operator. Among 2,411 patients (median follow-up: 42.7 months [IQR: 32.9-54.1]), 118 developed HCC (three-year cumulative incidence: 3.59%). Chronic hepatitis B virus infection was the main etiology, accounting for 91.5% of cases. The aMAP-CT model, which incorporates CT signatures, significantly outperformed existing models (area under the receiver-operating characteristic curve: 0.809-0.869 in three cohorts). It stratified patients into high-risk (three-year HCC incidence: 26.3%) and low-risk (1.7%) groups. Stepwise application (aMAP → aMAP-CT) further refined stratification (three-year incidences: 1.8% [93.0% of the cohort] vs. 27.2% [7.0%]). The aMAP-CT model improves HCC risk prediction by integrating CT-based liver and spleen signatures, enabling precise identification of high-risk cirrhosis patients. This approach personalizes surveillance strategies, potentially facilitating earlier detection and improved outcomes.

To evaluate the image quality and clinical utility of ultra-fast T2-weighted imaging (UF-T2WI), which acquires all slice data in 7 s using a single-shot turbo spin-echo technique combined with dual-type deep learning (DL) reconstruction, incorporating DL-based image denoising and super-resolution processing, by comparing UF-T2WI with conventional T2WI. We analyzed data from 38 patients who underwent both conventional T2WI and UF-T2WI with the dual-type DL-based image reconstruction. Two board-certified radiologists independently performed blinded qualitative assessments of the patients' images obtained with UF-T2WI with DL and conventional T2WI, evaluating the overall image quality, anatomical structure visibility, and levels of noise and artifacts. In cases that included central nervous system diseases, the lesions' delineation was also assessed. The quantitative analysis included measurements of signal-to-noise ratios in white and gray matter and the contrast-to-noise ratio between gray and white matter. Compared to conventional T2WI, UF-T2WI with DL received significantly higher ratings for overall image quality and lower noise and artifact levels (p < 0.001 for both readers). The anatomical visibility was significantly better in UF-T2WI for one reader, with no significant difference for the other reader. The lesion visibility in UF-T2WI was comparable to that in conventional T2WI. Quantitatively, the SNRs and CNRs were all significantly higher in UF-T2WI than conventional T2WI (p < 0.001). The combination of SSTSE with dual-type DL reconstruction allows for the acquisition of clinically acceptable T2WI images in just 7 s. This technique shows strong potential to reduce MRI scan times and improve clinical workflow efficiency.

Insomnia disorder is a prevalent condition associated with significant negative impacts on health and daily functioning. Transcranial direct current stimulation (tDCS) has emerged as a potential technique for improving sleep. However, questions remain regarding its clinical efficacy, and there is a lack of standardized individualized stimulation protocols. This study aims to evaluate the efficacy of model-driven, individualized tDCS for treating insomnia disorder through a randomized, double-blind, sham-controlled trial. A total of 40 patients diagnosed with insomnia disorder will be recruited and randomly assigned to either an active tDCS group or a sham stimulation group. Individualized stimulation parameters will be determined through machine learning-based electric field modeling incorporating structural MRI and EEG data. Participants will undergo 10 sessions of tDCS (5 days/week for 2 consecutive weeks), with follow-up assessments conducted at 2 and 4 weeks after treatment. The primary outcome is the reduction in the Insomnia Severity Index (ISI) score at two weeks post-treatment. Secondary outcomes include changes in sleep parameters, anxiety, and depression scores. This study is expected to provide evidence for the effectiveness of individualized tDCS in improving sleep quality and reducing insomnia symptoms. This integrative approach, combining advanced neuroimaging and electrophysiological biomarkers, has the potential to establish an evidence-based framework for individualized brain stimulation, optimizing therapeutic outcomes. This study is registered at ClinicalTrials.gov (Identifier: NCT06671457) and was registered on 4 November 2024. The online version contains supplementary material available at 10.1186/s12888-025-07347-5.

With increasing imaging demands, radiologists face growing workload pressures, often resulting in delays and reduced diagnostic efficiency. Recent advances in artificial intelligence (AI) have introduced tools for automated report generation, particularly in simpler imaging modalities, such as X-rays. However, limited research has assessed AI performance in complex studies such as MRI and CT scans, where report accuracy and clinical interpretation are critical. To evaluate the performance of a semi-automated AI-based reporting platform in generating radiology reports for complex imaging studies, and to compare its accuracy, efficiency, and user confidence with the traditional dictation method. This study involved 100 imaging cases, including MRI knee (n=21), MRI lumbar spine (n=30), CT head (n=23), and CT Abdomen and Pelvis (n=26). Consultant musculoskeletal radiologists reported each case using both traditional dictation and the AI platform. The radiologist first identified and entered the key positive findings, based on which the AI system generated a full draft report. Reporting time was recorded, and both methods were evaluated on accuracy, user confidence, and overall reporting experience (rated on a scale of 1-5). Statistical analysis was conducted using two-tailed t-tests and 95% confidence intervals. AI-generated reports demonstrated significantly improved performance across all parameters. The mean reporting time reduced from 6.1 to 3.43 min (p<0.0001) with AI-assisted report generation. Accuracy improved from 3.81 to 4.65 (p<0.0001), confidence ratings increased from 3.91 to 4.67 (p<0.0001), and overall reporting experience favored using the AI platform for generating radiology reports (mean 4.7 vs. 3.69, p<0.0001). Minor formatting errors and occasional anatomical misinterpretations were observed in AI-generated reports, but could be easily corrected by the radiologist during review. The AI-assisted reporting platform significantly improved efficiency and radiologist confidence without compromising accuracy. Although the tool performs well when provided with key clinical findings, it still requires expert oversight, especially in anatomically complex reporting. These findings support the use of AI as a supportive tool in radiology practice, with a focus on data integrity, consistency, and human validation.

BackgroundThe diagnostic performance of ultrasound (US) is heavily reliant on the operator's expertise. Advances in artificial intelligence (AI) have introduced deep learning (DL) tools that detect morphology beyond human perception, providing automated interpretations.PurposeTo evaluate Smart-Detect (S-Detect), a DL tool, for its potential to enhance diagnostic precision and standardize US assessments among radiologists with varying levels of experience.Material and MethodsThis prospective observational study was conducted between May and November 2024. US and S-Detect analyses were performed by a breast imaging fellow. Images were independently analyzed by five radiologists with varying experience in breast imaging (<1 year-15 years). Each radiologist assessed the images twice: without and with S-Detect. ROC analyses compared the diagnostic performance. True downgrades and upgrades were calculated to determine the biopsy reduction with AI assistance. Kappa statistics assessed radiologist agreement before and after incorporating S-Detect.ResultsThis study analyzed 230 breast masses from 216 patients. S-Detect demonstrated high specificity (92.7%), PPV (92.9%), NPV (87.9%), and accuracy (90.4%). It enhanced less experienced radiologists' performance, increasing the sensitivity (85% to 93.33%), specificity (54.5% to 73.64%), and accuracy (70.43% to 83.91%; <i>P</i> <0.001). AUC significantly increased for the less experienced radiologists (0.698 to 0.835 <i>P</i> <0.001), with no significant gains for the expert radiologist. It also reduced variability in assessment between radiologists with an increase in kappa agreement (0.459-0.696) and enabled significant downgrades, reducing unnecessary biopsies.ConclusionThe DL tool improves diagnostic accuracy, bridges the expertise gap, reduces reliance on invasive procedures, and enhances consistency in clinical decisions among radiologists.

To evaluate the diagnostic interchangeability, image quality, and scan time of deep learning (DL)-reconstructed magnetic resonance imaging (MRI) compared with conventional MRI for the temporomandibular joint (TMJ). Patients with suspected TMJ disorder underwent sagittal proton density-weighted (PDW) and T2-weighted fat-suppressed (T2W FS) MRI using both conventional and DL reconstruction protocols in a single session. Three oral radiologists independently assessed disc shape, disc position, and joint effusion. Diagnostic interchangeability for these findings was evaluated by comparing interobserver agreement, with equivalence defined as a 95% confidence interval (CI) within ±5%. Qualitative image quality (sharpness, noise, artifacts, overall) was rated on a 5-point scale. Quantitative image quality was assessed by measuring the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in the condyle, disc, and background air. Image quality scores were compared using the Wilcoxon signed-rank test, and SNR/CNR using paired t-tests. Scan times were directly compared. A total of 176 TMJs from 88 patients (mean age, 37 ± 16 years; 43 men) were analyzed. DL-reconstructed MRI demonstrated diagnostic equivalence to conventional MRI for disc shape, position, and effusion (equivalence indices < 3%; 95% CIs within ±5%). DL reconstruction significantly reduced noise in PDW and T2W FS sequences (p < 0.05) while maintaining sharpness and artifact levels. SNR and CNR were significantly improved (p < 0.05), except for disc SNR in PDW (p = 0.189). Scan time was reduced by 49.2%. DL-reconstructed TMJ MRI is diagnostically interchangeable with conventional MRI, offering improved image quality with a shorter scan time. Question Long MRI scan times in patients with temporomandibular disorders can increase pain and motion-related artifacts, often compromising image quality in diagnostic settings. Findings DL reconstruction is diagnostically interchangeable with conventional MRI for assessing disc shape, disc position, and effusion, while improving image quality and reducing scan time. Clinical relevance DL reconstruction enables faster and more tolerable TMJ MRI workflows without compromising diagnostic accuracy, facilitating broader adoption in clinical settings where long scan times and motion artifacts often limit diagnostic efficiency.

Endoscopic ultrasound (EUS) is crucial for diagnosing and managing mediastinal diseases but lacks effective quality control. This study developed and evaluated an artificial intelligence (AI) system to assist in anatomical landmark identification and scanning guidance, aiming to improve quality control of mediastinal EUS examinations in clinical practice. The AI system for mediastinal EUS was trained on 11,230 annotated images from 120 patients, validated internally (1,972 images) and externally (824 images from three institutions). A single-center randomized controlled trial was designed to evaluate the effect of quality control, which enrolled patients requiring mediastinal EUS, randomized 1:1 to AI-assisted or control groups. Four endoscopists performed EUS, with the AI group receiving real-time AI feedback. The primary outcome was standard station completeness; secondary outcomes included structure completeness, procedure time, and adverse events. Blinded analysis ensured objectivity. Between 16 September 2023, and 28 February 2025, a total of 148 patients were randomly assigned and analyzed, with 72 patients in the AI-assisted group and 76 in the control group. The overall station completeness was significantly higher in the AI-assisted group than in the control group (1.00 [IQR, 1.00-1.00] vs. 0.80 [IQR, 0.60-0.80]; p < 0.001), with the AI-assisted group also demonstrating significantly higher anatomical structure completeness (1.00 [IQR, 1.00-1.00] vs. 0.85 [IQR, 0.62-0.92]; p < 0.001). However, no significant differences were found for station 2 (subcarinal area) or average procedural time, and no adverse events were reported. The AI system significantly improved the scan completeness and shows promise in enhancing EUS quality control.

To deploy an AI model to measure limb-length discrepancy (LLD) and prospectively validate its performance. We encoded the inference of an LLD AI model into a docker container, incorporated it into a computational platform for clinical deployment, and conducted two prospective validation studies: a shadow trial (07/2024-9/2024) and a clinical trial (11/2024-01/2025). During each trial period, we queried for LLD EOS scanograms to serve as inputs to our model. For the shadow trial, we hid the AI-annotated outputs from the radiologists, and for the clinical trial, we displayed the AI-annotated output to the radiologists at the time of study interpretation. Afterward, we collected the bilateral femoral and tibial lengths from the radiology reports and compared them against those generated by the AI model. We used median absolute difference (MAD) and interquartile range (IQR) as summary statistics to assess the performance of our model. Our shadow trial consisted of 84 EOS scanograms from 84 children, with 168 femoral and tibial lengths. The MAD (IQR) of the femoral and tibial lengths were 0.2 cm (0.3 cm) and 0.2 cm (0.3 cm), respectively. Our clinical trial consisted of 114 EOS scanograms from 114 children, with 228 femoral and tibial lengths. The MAD (IQR) of the femoral and tibial lengths were 0.3 cm (0.4 cm) and 0.2 cm (0.3 cm), respectively. We successfully employed a computational platform for seamless integration and deployment of an LLD AI model into our clinical workflow, and prospectively validated its performance. Question No AI models have been clinically deployed for limb-length discrepancy (LLD) assessment in children, and the prospective validation of these models is unknown. Findings We deployed an LLD AI model using a homegrown platform, with prospective trials showing a median absolute difference of 0.2-0.3 cm in estimating bone lengths. Clinical relevance An LLD AI model with performance comparable to that of radiologists can serve as a secondary reader in increasing the confidence and accuracy of LLD measurements.