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PubMed2026

RR-interval-based atrial fibrillation detection and burden estimation: cross-dataset validation and calibration-aware probability analysis.

Objective.Atrial fibrillation (AF) burden has become an increasingly important endpoint in long-duration rhythm monitoring, but reliable burden estimation requires more than accurate AF detection alone. In particular, when burden is derived by aggregating predicted AF probabilities over time, probability calibration may directly affect burden validity under external dataset shift.Approach.This study developed an interpretable-interval feature model for AF detection and evaluated it using record-wise cross-validation on a development cohort and independent cross-dataset external validation on public Holter electrocardiographic databases. Window-level performance was assessed using the area under the receiver operating characteristic curve (ROC-AUC), area under the precision-recall curve (PR-AUC), Brier score, expected calibration error (ECE), and calibration intercept and calibration slope. Recording-level AF burden was estimated using both probability-based and hard-label aggregation and evaluated using mean absolute error (MAE) and agreement analyses.Main results.The model showed high discrimination in both development and external evaluation, with external ROC-AUC ofand PR-AUC of. However, external calibration deteriorated despite preserved ranking performance, with Brier score of, ECE(15) of, calibration intercept of, and calibration slope of. In the external cohort, probability-based burden estimation preserved strong association with reference burden but showed weaker raw agreement than hard-label aggregation, with MAE ofversus, consistent with systematic probability underprediction. Repeated external recalibration across record-level splits substantially improved probability quality and probability-based burden estimation. Median probability-burden MAE decreased fromwithout recalibration toafter Platt recalibration andafter isotonic recalibration, while median ECE(15) decreased fromtoand, respectively.Significance.These findings indicate that-interval-based AF detection maintained strong ranking performance in the tested external cohort, but probability calibration should be evaluated explicitly when predicted probabilities are aggregated into AF-burden estimates.

Atrial Fibrillation↗

PubMed2026

An automated geometric modeling framework in GATE for the design and optimization of high-sensitivity converging-beam SPECT collimators.

Objective.The trade-off between detection sensitivity and spatial resolution is a fundamental challenge in designing organ-dedicated Single-photon emission computed tomography (SPECT) collimators. While converging-hole geometries offer a solution, their optimization is often hindered by the lack of flexible computational tools capable of modeling large-scale, non-parallel hole arrays. This study aims to develop an automated geometric modeling framework to facilitate the design and evaluation of complex converging- and diverging-hole collimators within standard Monte Carlo environments.Approach.We developed a specialized modeling framework by implementing custom C++ classes and a vector-based alignment algorithm within GATE. This platform enables automated, orientation-consistent construction of large-scale converging arrays not natively supported by standard implementations. A high-sensitivity pure cone-beam collimator (CBC) was designed using this framework. The evaluation used hot-rod, disc, and Jaszczak phantoms for physical characterization, while XCAT and dedicated brain models were employed for clinical tasks, including cardiac, brain perfusion, and DaTscan SPECT simulations.Main results.The CBC achieved a nearly fourfold sensitivity increase compared to a conventional low-energy high-resolution parallel-hole collimator at a 20 cm radius of rotation, while maintaining comparable spatial resolution. Despite a 52.3% field of view reduction, the CBC yielded a 2.2-fold noise reduction (CV: 11.7% vs 25.9%) and mitigated partial volume effects via geometric magnification. XCAT and brain phantom simulations confirmed enhanced anatomical definition and contrast recovery in cardiac, perfusion, and DaTscan tasks.Significance.This work provides an efficient computational tool for rapid design space exploration of advanced collimator geometries. The results demonstrate that the proposed CBC design offers a significant sensitivity advantage, making it highly suitable for high-performance, small-volume clinical applications such as brain and cardiac molecular imaging.

Tomography, Emission-Computed, Single-Photon↗

PubMed2026

Reliability-aware hierarchical learning for Chagas disease screening from 12-lead ECGs: tackling label uncertainty and class imbalance.

Objective.Chagas disease, a neglected tropical disease (NTD) with significant cardiovascular impact, remains underdiagnosed in resource-limited regions. Electrocardiogram (ECG) screening offers a low-cost tool for detecting cardiac involvement, yet algorithm development is challenged by label noise, data scarcity, and the latent nature of infection. This study proposes a robust ECG-based screening framework that explicitly addresses these constraints.Approach.We introduce aReliability-Aware Hierarchical Learningstrategy that calibrates supervision according to data provenance, prioritizing serology-confirmed labels over noisy self-reports. To mitigate data scarcity, we compare a specialized convolutional neural network (CNN) trained from scratch with a transfer learning approach based on a Spatio-Temporal ECG foundation Model (FM). Performance is evaluated across varying data scales, and the representation structure is analyzed to interpret model behavior.Main results.On the official hidden test set of the George B. Moody PhysioNet/Computing in Cardiology Challenge 2025, our approach achieved a Challenge Score of 0.163. We observe that while the specialized CNN performs competitively in data-rich regimes, the FM exhibits superior robustness in extreme low-resource settings. Furthermore, performance reaches a plateau imposed by underlying disease physiology. Bimodal score distributions suggest that models distinguish established cardiomyopathy from indeterminate infection, which remains electrophysiologically indistinguishable from healthy controls.Significance.These findings clarify both the potential and intrinsic limits of ECG-based AI screening for NTD-associated cardiac involvement. Reliability-aware supervision and data-efficient transfer learning provide a practical framework toward scalable and clinically meaningful ECG screening systems in resource-constrained environments.

Humans↗

PubMed2026

A multi-scale fusion model based on multi-phase contrast-enhanced CT for predicting pancreatic cancer resectability.

Purpose.Develop a multi-scale fusion model (MSFM) based on multi-phase contrast-enhanced computed tomography (CECT) to predict pancreatic cancer (PC) resectability, thereby assisting expert decision-making.Methods.This retrospective study enrolled 280 patients with PC from four institutions, which were randomly divided into a training cohort (202 patients) and an independent test cohort (78 patients). Three-phase CECT images (arterial, venous, and delayed phases) were used for modeling. The MSFM comprises two sub-networks: (1) a multi-phase fusion network for extracting cross-phase shared fusion features, (2) a phase-specific branch network for capturing phase-specific features; and a post-fusion strategy to generate the final predictive score by integrating the shared fusion features and three groups of phase-specific features. Additionally, a human-machine fusion deep learning model (HMfDL) was constructed by fusing the predictive score of the MSFM with expert assessments.Results.In the independent test, the MSFM achieved an AUC (area under the receiver operating characteristic curve) of 0.8385 (95% CI: 0.7521-0.9249), accuracy of 84.62%, sensitivity of 72.00%, and specificity of 90.57%. This performance outperformed single-phase models (AUC range: 0.7638-0.7781), two-phase models (AUC range: 0.7826-0.7864), and ten states-of-the-art classifiers (AUC range: 0.7404-0.7796). The HMfDL further improved the performance, reaching an AUC of 0.8626 (95% CI: 0.7853-0.9400), accuracy of 91.03%, sensitivity of 80.00%, and specificity of 96.23%. Notably, the HMfDL corrected 58.82% of misdiagnosis made by experts.Conclusions. The MSFM effectively fuses multi-phase CECT to enable highly accurate predictions of PC resectability, and provides valuable support for expert decision-making through HMfDL.

Humans↗

PubMed2026

A STORM-based protocol for nanoscale imaging and quantitative analysis of protein-associated and phospholipid-associated structures in natural rubber.

Stochastic Optical Reconstruction Microscopy (STORM) enables nanoscale mapping of molecular components beyond the diffraction limit; however, its reproducible implementation in hydrophobic polymer matrices remains challenging because fluorescence-labeling specificity, fluorophore photoswitching, three-dimensional localization, chromatic registration, and quantitative image analysis must be carefully controlled. This protocol presents a standardized experimental workflow for dual-color labeling, astigmatism-based three-dimensional STORM acquisition, and quantitative analysis of protein-associated and phospholipid-associated structures in natural rubber (NR). The workflow covers sample pretreatment, Cy5 NHS ester labeling of protein-associated primary amines, DiI labeling of phospholipid-rich domains, STORM imaging-buffer preparation, three-dimensional single-molecule localization, dual-channel registration, generation of standardized xy projections, aggregate-size analysis, and projected lateral spatial correlation assessment. Reproducibility is supported by defined acquisition and localization criteria, three independent sample preparations with at least five fields of view analyzed per condition, and unlabeled, single-color, dye-only matrix, and processing-associated Cy5 controls. Mean lateral localization precisions of 11.8 ± 2.3 nm for Cy5 and 13.5 ± 2.9 nm for DiI were obtained, while two-dimensional Fourier ring correlation analysis of the xy projections yielded effective lateral image resolutions of approximately 25 and 28 nm, respectively. Image-based particle segmentation and localization-coordinate-based density-based spatial clustering of applications with noise (DBSCAN) were applied to standardized xy projections as complementary quantitative approaches. Application of the protocol to untreated, centrifuged, and protease-treated NR samples demonstrated treatment-associated changes in the detected abundance and projected size distributions of protein- and phospholipid-associated aggregates, together with a non-monotonic change in their projected lateral spatial correlation. These observations describe alterations in nanoscale organization but do not, by themselves, establish stable protein-phospholipid complex formation. Unlike previous studies that primarily demonstrated the feasibility of STORM imaging in rubber materials, the principal contribution of this work is an end-to-end, step-by-step protocol incorporating defined controls, three-dimensional localization, image-quality metrics, chromatic-registration procedures, and complementary quantitative-analysis pipelines for non-expert users. The workflow may be adaptable to other hydrophobic polymers and soft-material systems after appropriate optimization and validation.

Rubber↗

PubMed2026

Failure modes and effects analysis for clinical implementation of online adaptive radiotherapy: A systematic review.

BACKGROUND: The accuracy of radiotherapy is limited by anatomical variations occurring over time scales ranging from sub-seconds to days. Online Adaptive Radiotherapy (OART) addresses this by enabling daily plan adaptation based on real-time imaging. While OART offers improved dose conformity, its dynamic, time-constrained workflow introduces novel failure modes that challenge traditional quality assurance protocols. PURPOSE: This study aims to synthesize the existing literature on Failure Modes and Effects Analysis (FMEA) for OART to systematically catalog risks and identify mitigation strategies. METHODS: A systematic literature search was conducted to identify studies applying FMEA to OART workflows. Eleven studies were included, covering MR-guided (ViewRay MRIdian, Elekta Unity), CBCT-guided (Varian Ethos), and MR-enhanced C-arm linac systems. To address heterogeneity in risk scoring methodologies (e.g., TG-100 10-point scales vs. 5-point rankings), extracted failure modes were harmonized into a standardized three-tier risk classification system (Class I: Low, Class II: Intermediate, Class III: High). RESULTS: A total of 300 unique failure modes were identified, with 49.6 percent classified as high-risk (Class III). Analysis revealed that the majority of high-risk failures were concentrated in the online treatment delivery phase, specifically within human-computer interactions and anatomical contouring steps. CONCLUSIONS: This study supports the development of tailored, robust QA frameworks that prioritize human factors and process consistency to guide safe implementation in diverse clinical settings.

Humans↗

PubMed2026

Analysis of deep learning techniques in computer-aided diagnosis for meniscus injuries: a systematic literature review.

Meniscus informatics is a growing subject of study in the healthcare industry. One of the major hindrances to the healthcare system's transformation is obtaining knowledge and meaningful information from complicated, high-dimensional and diverse sources. Modern biomedical research, for instance, has seen an increase in the use of complex, dissimilar, poorly documented, and generally unstructured electronic health records, imaging, sensor data and text, even after many current techniques have been used to extract more robust and useful elements from the data for analysis. New efficient standards for building end-to-end learning models from complex data are therefore needed. Therefore, the current study aims to examine the most recent research on the use of deep learning techniques for diagnosing meniscus tears and recommend creating comprehensive and meaningful interpretable structures that might benefit the healthcare industry. We also draw attention to shortcomings and the need for better technique development, and we provide new perspectives about this exciting new development in the field.

Humans↗

PubMed2026

Post-intervention effectiveness of a computerized personalized cognitive stimulation program adapted according to cognitive reserve in older adults without cognitive impairment in Primary Care: A randomized clinical trial.

BACKGROUND: Cognitive reserve may influence responsiveness to cognitive interventions, yet it is rarely used to tailor computerized stimulation. OBJECTIVE: To evaluate the effectiveness of a computerized cognitive stimulation program personalized according to cognitive reserve on cognition, reserve-related activities, and digital competence in community-dwelling older adults without cognitive impairment in Primary Care. METHODS: In this randomized clinical trial, 102 adults aged ≥65 years with normal cognitive performance were recruited from three primary care centers in Zaragoza, Spain, and stratified by cognitive reserve level before random allocation to intervention or control. The intervention comprised digital literacy sessions followed by 8 weeks of home-based computerized cognitive stimulation tailored to participants' cognitive reserve profiles and life history. Controls received a single group-based health education session focused on maintaining everyday cognitive activity. Outcomes were assessed at baseline and post-intervention using global cognition (MEC-35), the Cognitive Reserve Questionnaire, the Mobile Device Proficiency Questionnaire-16, and domain-specific neuropsychological tests. A total of 100 participants completed the final evaluation and were included in complete-case analyses. RESULTS: Compared with controls, the intervention group showed greater adjusted post-intervention improvements in global cognition (MEC-35 between-group difference: 1.8 points) and several cognitive measures, including temporal orientation, calculation, attention, praxis, verbal fluency, processing speed, executive functions, and verbal learning. CRQ scores and digital competence also improved, with small-to-large effect sizes. CONCLUSIONS: A computerized cognitive stimulation program adapted according to cognitive reserve appears feasible in Primary Care and may improve cognition, engagement in reserve-related activities, and digital competence in older adults without cognitive impairment.

Humans↗

PubMed2026

AI echo INSIGHT study: A prospective blinded randomized trial of artificial intelligence echocardiogram interpretation.

BACKGROUND: Transthoracic echocardiography (TTE) is the most commonly performed cardiac imaging modality with over 30 million studies annually. Demand for timely expert interpretation continues to outpace capacity, creating diagnostic delays and inter-observer variability that impact patient care. Recent research has suggested computer vision artificial intelligence (AI) models can generate accurate preliminary comprehensive TTE reports, however, prospective evaluation is needed to determine whether AI-assisted TTE interpretation can improve clinician efficiency while preserving diagnostic accuracy. METHODS: AI ECHO INSIGHT is a prospective randomized blinded clinical trial conducted at Kaiser Permanente Northern California that will evaluate 1200 historical TTE studies (1000 consecutive unselected studies plus 200 with moderate or greater valvular disease) interpreted using three workflows: (1) AI-generated preliminary report finalized by a blinded cardiologist (AI-assisted); (2) cardiologist-generated preliminary report finalized by a blinded cardiologist (cardiologist-assisted); and (3) sonographer-generated preliminary report finalized by a blinded cardiologist (sonographer-assisted). The primary outcome is the rate of substantial change between preliminary and final reports, comparing the AI-assisted workflow to the pooled cardiologist-assisted and sonographer-assisted workflows. Secondary outcomes include cardiologist interpretation time for report finalization, superiority testing for diagnostic accuracy, and reporting consistency. CONCLUSION: AI ECHO INSIGHT is a prospective randomized blinded clinical trial evaluating the clinical impact of AI-assisted TTE interpretation on diagnostic accuracy, cardiologist efficiency, and reporting consistency in real-world echocardiography workflows. TRIAL REGISTRATION: ClinicalTrials.gov registration number NCT07229300.

Humans↗

PubMed2026

Virtual surgical planning-assisted versus free-hand head and neck reconstruction: Systematic review, meta-analysis, and a novel classification.

Virtual surgical planning (VSP)-assisted reconstruction is increasingly used as an alternative to conventional free-hand (FH) techniques in mandibular and maxillary free-flap reconstruction. This systematic review and meta-analysis compared clinical outcomes and proposed a Reconstruction Complexity-Completeness classification. PubMed/MEDLINE, Scopus, Web of Science, Google Scholar, and reference lists were searched from inception to 20 June 2026. Comparative studies were eligible. Risk of bias was assessed using RoB 2 or the Newcastle-Ottawa Scale. Random-effects meta-analyses used restricted maximum likelihood estimation and Hartung-Knapp adjustment. Forty-two studies included 2763 patients (1204 VSP; 1559 FH). VSP significantly reduced operative time (33 studies; MD -64.75 min, 95% CI -83.51 to -46.00), ischemia time (15 studies; MD -37.40 min, 95% CI -48.97 to -25.82), and hospital stay (16 studies; MD -1.75 days, 95% CI -3.43 to -0.08). VSP was associated with significantly lower odds of bony non-union (OR 0.31, 95% CI 0.16-0.59) and malocclusion (OR 0.14, 95% CI 0.03-0.64), whereas flap loss, surgical site infection, and plate exposure did not differ significantly. VSP-assisted reconstruction was associated with improved operative efficiency, shorter hospitalization, and lower odds of bony non-union and malocclusion, while no statistically significant differences were detected in flap loss, surgical site infection, or plate exposure. The proposed classification may support complexity-adjusted reporting and comparison.

Humans↗

PubMed2026

Artificial neural network data fusion-mediated dual-mode sensor based on Fe3O4@PdIr for Salmonellatyphimurium detection in food.

Salmonella Typhimurium (S. typhimurium) is a major foodborne pathogen that poses a serious threat to public health. In this study, a colorimetric/electrochemical dual-mode biosensor assisted by artificial neural network (ANN) was developed for the sensitive detection of S. typhimurium. Fe3O4@PdIr nanocomposites with enhanced peroxidase-like activity and electrochemical performance were prepared and conjugated with an aptamer specific to S. typhimurium to obtain Fe3O4@PdIr-Apt. Through the sandwich binding of Fe3O4@PdIr-Apt and Apt to the target, the nanocomposites were attached to microplates or Au electrodes, thereby generating colorimetric and electrochemical signals. The ANN model deeply resolved the complex nonlinear relationship between the dual signals, enabling mutual correction and ultimately performing data fusion to output a single detection result, which significantly reduced the mean square error while improving detection sensitivity and reliability. This sensor exhibited a wide linear range of 2.7-2.7 × 108 CFU/mL and a low detection limit of 1.66 CFU/mL. Additionally, this method was successfully applied to the detection of S. typhimurium in pork and milk, with a recovery rate of 95.19% ∼ 104.07%. It indicated that the constructed sensor holds great practical potential for S. typhimurium detection.

Neural Networks, Computer↗

PubMed2026

Reinforcement learning-based dynamic ensemble for missense variant effect prediction and tiered prioritization of VUS.

BACKGROUND: Accurate classification of missense variants remains a challenging task despite major advances in genomics. Numerous computational models have been developed to assist in variant classification, but often require repeated integration and benchmarking efforts. Ensemble methods have been proposed to overcome the limitations of single predictors, but mostly rely on fixed, predefined weights that constrain their ability to capture interactions among predictive signals. METHODS: We present GenixRL, a dynamic ensemble framework that reformulates model fusion as a reinforcement learning optimization problem. GenixRL uses a Q-learning agent to learn a policy that dynamically weights the probabilistic outputs of complementary predictors, including BayesDel (addAF and noAF), ClinPred, and MetaRNN. Replacing static weighting with policy learning allows GenixRL to adaptively identify optimal weightings and substantially improve classification accuracy. RESULTS: In benchmark evaluation against 25 state-of-the-art predictors, GenixRL achieved an AUROC of 0.9644 on an independent ClinVar dataset. On saturation genome editing assays for BRCA1 and BRCA2, GenixRL achieved the best performance and ranked highest on 14 of 17 clinically significant genes in a zero-shot evaluation. Applied to uncertain and conflicting ClinVar variants, GenixRL enabled tiered, evidence-based prioritization of hundreds of thousands of variants as likely pathogenic or pathogenic with high confidence, supported by orthogonal population evidence from gnomAD. CONCLUSION: GenixRL advances pathogenicity prediction for missense variants and provides an adaptive ensemble that sorts variants of uncertain significance into tiered candidates for expert curation and functional validation.

Mutation, Missense↗

PubMed2026

Linked-color imaging with computer-aided detection and the proximal adenoma miss rate: a randomized tandem trial.

BACKGROUND AND AIMS: Linked-color imaging (LCI) aids the detection and characterization of lesions. Computer-aided detection (CADe) systems have been introduced to improve lesion detection during colonoscopy. Although several studies have been reported regarding LCI, few have investigated the combination of LCI and CADe. This study aimed to evaluate the efficacy of LCI with CADe colonoscopy compared to conventional white-light colonoscopy. METHODS: A single-center, randomized tandem trial was conducted. Participants referred for first-time colonoscopy after fecal immunochemical test (FIT)-positive, asymptomatic screening, or surveillance colonoscopy were randomized (1:1) to undergo CADe-assisted colonoscopy of LCI or white-light imaging (WLI) in the right side of the colon. The primary outcome was adenoma miss rate (AMR) in the right side of the colon. Secondary outcomes included polyp miss rate (PMR), diminutive adenoma miss rate (dAMR), sessile serrated lesion miss rate (SSLMR), advanced adenoma miss rate, advanced neoplasia miss rate, flat-type lesion miss rate (FMR), and the differences in miss rates based on expertise. RESULTS: Among 232 randomized participants, 209 were analyzed (LCI/CADe: 102; WLI: 107). AMR (WLI: 39% vs LCI/CADe: 20%; P = .001), PMR (42% vs 18%; P < .001), and dAMR (42% vs 21%; P = .003) were significantly lower in the LCI/CADe arm, particularly among experts. SSLMR (46% vs 0%), advanced AMR (30% vs 0%), advanced neoplasia miss rate (25% vs 0%), and FMR (27% vs 5.6%) were lower in LCI/CADe, although without statistical significance. CONCLUSIONS: Compared to conventional colonoscopy, LCI with CADe colonoscopy resulted in a statistically significant decrease, especially in AMR. (UMIN 000050685).

Humans↗

PubMed2026

Three-Dimensional Fracture Mapping of the Terrible Triad of the Elbow: Morphological Characteristics and Clinical Implications.

BACKGROUND: The morphology of fractures in the terrible triad of the elbow (TTE) is complex, and precise management relies on a profound understanding of this morphology. This study aims to systematically analyze, for the first time, the distribution and morphological characteristics of TTE fracture lines using three-dimensional (3D) imaging technology. METHODS: Clinical data and thin-slice CT scans of 112 patients with TTE from January 2021 to December 2024 were retrospectively included. 3D fracture models were reconstructed using Mimics software. Virtual reduction and standardized alignment were performed using 3-matic software. Fracture lines were mapped onto standard ulnar and radial templates, and 3D fracture heat maps were generated using the E-3D software to demonstrate the high-frequency distribution zones of the fracture lines visually. Statistical analysis was performed using SPSS software (version 21.0, IBM Corp., Armonk, NY, USA). Continuous variables were compared using one-way analysis of variance (ANOVA), and categorical variables were compared using the chi-square test (χ2 test). A two-tailed p < 0.05 was considered statistically significant. RESULTS: The study revealed distinct patterns in the distribution of TTE fracture lines. In the coronoid process, the fracture "hot zone" presented as an annular high-density band extending from the lateral middle aspect to the tip. In the radial head, an oblique high-density band was observed in the anterolateral quadrant of the articular surface. The radial neck exhibited a circumferential high-density zone, which was most prominent in the anterolateral aspect. Statistical analysis indicated a significant correlation between age and fracture complexity; the proportion of Regan-Morrey type III coronoid fractures and Mason type III radial head fractures was significantly higher in elderly patients (> 60 years) (p < 0.05), suggesting that advanced age is a significant risk factor for complex fractures. CONCLUSION: This study is the first to visually reveal the Collaborative Distribution Patterns of TTE fracture lines using 3D fracture mapping technology. This model provides morphological evidence for understanding the injury mechanism of TTE and offers an anatomical framework that may assist surgeons in individualizing surgical approaches and fixation strategies.

Humans↗

PubMed2026

Artificial intelligence-assisted detection and optical differentiation of colorectal lesions in Lynch syndrome surveillance (CADLY2): a multicentre, open-label, randomised controlled superiority trial.

BACKGROUND: Artificial intelligence (AI)-based computer-aided detection (CADe) systems improve adenoma detection in average-risk colorectal cancer screening. Meanwhile, evidence in Lynch syndrome surveillance is sparse and inconsistent. We assessed the effect of CADe on adenoma detection during Lynch syndrome surveillance. Computer-aided optical diagnosis (CADx) performance for optical differentiation of colorectal lesions was evaluated as a secondary aim. METHODS: CADLY2 was an international, multicentre, open-label, randomised controlled superiority trial at nine specialised hereditary cancer surveillance centres in Belgium, Germany, the Netherlands, and Spain. Adults aged 18 years or older with genetically confirmed Lynch syndrome scheduled for surveillance colonoscopy were randomly assigned (1:1) to high-definition white-light (HD-WL) colonoscopy alone or to HD-WL colonoscopy with computer-aided assistance from CAD EYE (Fujifilm, Tokyo, Japan). CAD EYE was used for CADe during withdrawal and for CADx after lesion detection. Randomisation was done centrally through a secure web-based system using Pocock's minimisation algorithm with a stochastic component and was stratified by centre, sex, previous colorectal cancer, underlying pathogenic variant, and interval since previous colonoscopy. Allocation concealment was ensured through the centralised web-based system. Patients were masked to group allocation until the start of withdrawal in procedures with mild sedation, or until completion of the procedure in procedures with propofol-based sedation. Endoscopists were not masked. The primary outcome was adenoma detection rate, defined as the proportion of patients with at least one histopathologically confirmed adenoma, analysed in the full analysis set (defined as all randomly allocated patients with available data for the primary outcome). The diagnostic performance of the CADx system was evaluated as a secondary outcome. The safety analysis set comprised all randomly allocated patients who underwent a study colonoscopy. This study is registered with the German Clinical Trials Register, DRKS00030695, and is completed. FINDINGS: Between May 9, 2023, and Oct 30, 2025, 757 patients were randomly allocated to HD-WL colonoscopy (377 patients) or to AI-assisted colonoscopy (380 patients); 733 patients were included in the full analysis set (369 HD-WL and 364 AI-assisted). The median age was 49 years (IQR 38-59) in the HD-WL group and 50 years (38-59) in the AI-assisted group; 213 (58%) were female and 156 (42%) male in the HD-WL group, and 207 (57%) were female and 157 (43%) male in the AI-assisted group. The adenoma detection rate was 30·9% (114 of 369 patients) with HD-WL versus 33·8% (123 of 364 patients) with CADe assistance (odds ratio 1·14 [95% CI 0·83-1·57], p=0·41). For CADx differentiation of neoplastic versus non-neoplastic lesions in the paired lesion-level analysis, with histopathology as the reference standard and sessile serrated lesions and traditional serrated adenomas classified as non-neoplastic, CADx sensitivity was 85·9% (95% CI 82·0-89·1) and specificity was 91·4% (89·4-93·0). Three adverse events occurred in the AI-assisted group: two mild post-polypectomy bleedings and one serious pulmonary embolism or deep venous thrombosis unrelated to the procedure. No adverse events occurred in the HD-WL group. INTERPRETATION: CADe-assisted colonoscopy did not show the absolute improvement in adenoma detection rate that was assumed in the prespecified sample-size calculation. CADx did not clearly improve lesion differentiation beyond expert optical diagnosis in expert Lynch syndrome surveillance settings. FUNDING: Third-party research funding of the National Center for Hereditary Tumor Syndromes, University Hospital Bonn.

Humans↗

PubMed2026

Wedge tarsectomy using patient specific instrumentation for complex multiplanar foot deformity Reconstruction: A prospective case series.

BACKGROUND: Bony correction in complex cavovarus deformities is often multiplanar. We examine our results following wedge tarsectomy (WT) using patient-specific instrumentation (PSI). METHODS: This single-centre, prospective case series evaluated noncorrectable cavovarus feet undergoing PSI-guided WT. Accuracy of PSI guides/plans, operative duration, and adjunctive procedures were recorded. Weightbearing CT (WBCT) measurements and PROM scores were recorded preoperatively and postoperatively, with 1 year follow-up. Data was then statistically analysed. RESULTS: Eleven patients were included. Planned correction was achieved (two required minor intraoperative adjustments to the initial osteotomy and nine required adjunctive procedures). Mean operative time was 135 min. Postoperative improvements were significant radiologically and in MOxFW walking distance. All fused by 3 months, with no significant complications. CONCLUSION: PSI-guided wedge tarsectomy safely achieves predictable multiplanar corrections. Our unit's experience has been excellent, with improvement in patients' walking, particularly with larger deformity corrections. LEVEL OF EVIDENCE: Level IV, prospective case series.

Humans↗

PubMed2026

PCa Detection in PI-RADS 4 and 5 Lesions: Comparison of [68Ga]Ga-PSMA-11 PET/CT-Guided Robot-Assisted Biopsy Versus mpMRI Cognitive-Fusion TRUS-Guided Prostate Biopsy.

Lesions with a Prostate Imaging-Reporting and Data System (PI-RADS) score of 4 or greater on multiparametric MRI (mpMRI) indicate a high likelihood of prostate cancer (PCa), and guidelines recommend a targeted biopsy. We aimed to compare the diagnostic performance of robotic arm-assisted [68Ga]Ga-PSMA-11 PET/CT-guided prostate biopsy (PGPB) with mpMRI-directed cognitive-fusion transrectal ultrasound-guided biopsy (MCFB) in biopsy-naïve men with clinical findings suggestive of PCa. Methods: This prospective, single-center, randomized clinical trial (NCT05137561) enrolled biopsy-naïve men age 50-90 y with elevated levels of prostate-specific antigen (≥4 ng/mL) and abnormal digital rectal examination findings. All participants underwent mpMRI, and those with a PI-RADS score of 4 or greater were randomized into 2 arms. In arm 1, participants underwent PGPB for a [68Ga]Ga-PSMA-avid lesion, and participants in arm 2 underwent MCFB. Participants in arm 1 with PET-negative findings subsequently underwent MCFB, and participants with negative biopsy results underwent PET and PGPB. The primary outcome was the detection of PCa. Secondary outcomes included complication rates and participant-reported pain. Result: Of the 267 participants enrolled, 81.3% (217) had lesions with a PI-RADS score of 4 or greater and were randomized to either PGPB (n = 112) or MCFB (n = 105). PCa was detected in 97.1% of participants (101/104) in arm 1 and 81.0% (85/105) in arm 2 (P < 0.05). PGPB showed higher diagnostic accuracy for PI-RADS 5 lesions (100% vs. 95.1%, P = 0.09). Major complications were observed in arm 2 only (n = 5). Arm 1 had significantly fewer complications (10.8% vs. 51.4%, P < 0.01), a lower median visual analog scale score for pain (3 vs. 5), and shorter procedure times. The core positivity rate was higher in arm 1 (60% ± 20%), despite obtaining fewer cores. Conclusion: [68Ga]Ga-PSMA-11 PGPB demonstrated higher diagnostic performance, fewer complications, and better tolerability compared with MCFB. This approach enables integrated diagnosis and staging, offering a promising alternative for efficient, safe, and accurate evaluation of prostate cancer.

Humans↗

PubMed2026

Robotic Needle Insertion for CT-guided Percutaneous Biopsy of Thoracoabdominal Lesions: A Prospective Multicenter Randomized Trial.

Purpose To compare safety and feasibility between a novel CT-guided robotic system and the conventional freehand technique for puncture biopsy of thoracoabdominal lesions. Materials and Methods In this prospective multicenter randomized trial, individuals with suspected lesions were enrolled between July 2023 and April 2024 across three university teaching hospitals and randomized to the robot-assisted group (n = 82) or the freehand group (n = 83). Procedure outcomes included the technical success rate, targeting error, number of CT scans and needle adjustments, puncture time, and complications. Descriptive and inferential statistics were calculated. Results A total of 165 participants (mean age, 60 years ± 10 [SD]; 83 male) were included. Compared with the freehand group, the robot-assisted group demonstrated a higher technical success rate (97.56% [80 of 82] vs 62.65% [52 of 83], P < .001), lower targeting error (mean Euclidean deviation: 1.7 mm ± 1.1 vs 4.5 mm ± 3.9, P < .001), and fewer CT scans (mean, 4.3 ± 1.9 vs 5.2 ± 2.3; P = .002) and needle adjustments (mean, 0.7 ± 0.7 vs 1.6 ± 1.6; P = .003). Despite differences in geometric precision, both groups achieved 100% (82 of 82 and 83 of 83) diagnostic yield. The median puncture time was comparable between groups (5.5 minutes ± 4.3 vs 4.8 minutes ± 7.0, P = .50). During lung biopsies, the robot-assisted approach yielded fewer complications compared with the freehand approach (4.88% [four of 82] vs 16.87% [14 of 83], P = .014). Conclusion Compared with the freehand approach, robot-assisted biopsy yielded greater precision and reduced adjustments and complications while demonstrating noninferior diagnostic efficacy and comparable duration. Keywords: Robotic Needle Insertion, Biopsy, Thoracoabdominal Lesions, Robot-assisted Biopsy, CT-guided Intervention, Percutaneous Needle Biopsy, Randomized Controlled Trial, Algorithm Development, CT, Clinical Testing, Interventional-Body, Biopsy/Needle Aspiration, Percutaneous, Thorax, Abdomen/GI, Liver, Lung, Kidney ©RSNA, 2026.

Humans↗