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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

Diagnostic Performance of Machine Learning for Systemic Lupus Erythematosus: Systematic Review and Meta-Analysis.

BACKGROUND: Early and accurate diagnosis of systemic lupus erythematosus (SLE) and its organ involvement is essential. Previous reviews of machine learning (ML) in SLE combined heterogeneous tasks and validation strategies and may have overinterpreted model performance. OBJECTIVE: This study evaluated the diagnostic performance of ML and deep learning (DL) models for 3 clinically distinct SLE-related tasks: SLE classification or diagnosis, lupus nephritis (LN) diagnosis, and neuropsychiatric systemic lupus erythematosus (NPSLE) discrimination. We also assessed methodological quality and certainty of evidence. METHODS: PubMed, Embase, Cochrane Library, Web of Science, and IEEE Xplore were searched from January 2014 to April 2026. Eligible peer-reviewed diagnostic accuracy studies developed or validated ML or DL models for 1 of the 3 prespecified tasks, used an accepted reference standard, and provided data for a 2×2 contingency table. Bivariate random-effects meta-analyses with the Hartung-Knapp-Sidik-Jonkman adjustment were used to pool sensitivity and specificity. We reported 95% prediction intervals (PIs), assessed risk of bias using the Quality Assessment of Diagnostic Accuracy Studies for Artificial Intelligence tool (QUADAS-AI; Viknesh Sounderajah [Imperial College London]), and evaluated certainty of evidence using the Grading of Recommendations Assessment, Development, and Evaluation framework for diagnostic test accuracy. RESULTS: Twenty-nine studies were included: 17 for SLE classification, 5 for LN diagnosis, and 7 for NPSLE discrimination. In the primary task-stratified analysis, pooled sensitivity was 0.91 (95% CI 0.86-0.94; 95% PI 0.56-0.99), and pooled specificity was 0.94 (95% CI 0.91-0.96; 95% PI 0.69-0.99), with low heterogeneity (I²=23.9% and 22.9%, respectively). DL models showed a sensitivity of 0.93 and specificity of 0.95, compared with 0.88 and 0.94 for traditional ML models. Certainty of evidence was high for most analyses but low for LN diagnosis because of inconsistency and imprecision. All studies were retrospective, and only 9 of 29 (31%) performed independent external validation. Overall risk of bias was high or unclear in 22 of 29 (75.9%) studies. No study reported model calibration, decision-curve analysis, or net clinical benefit. CONCLUSIONS: ML models showed promising diagnostic accuracy across 3 distinct SLE-related tasks, but wide PIs, limited external validation, and pervasive risk of bias restrict conclusions about real-world generalizability. Prospective multicenter studies with standardized tasks and reference standards, independent external validation, and formal assessment of calibration and clinical utility are required before clinical implementation.

Humans↗
PubMed1975

Diagnosis of liver diseases by laboratory results and discriminant analysis. Identification of best combinations of laboratory tests.

Patients with different liver diseases were studied by discriminant analysis. Groups of patients classified mainly on the basis of liver biopsy findings showed functional differences which permitted a consistent reclassification by discriminant functions using laboratory results. Optimal combinations of laboratory tests for the separation of liver diseases were defined. Different combinations were found, dependent on the subsets of liver diseases studied.

Acute Disease↗
PubMed1978

Evaluating clustering methods for psychiatric diagnosis.

This report represents an empirical evaluation of the major clustering approaches on psychiatric diagnostic data. Experienced psychiatrists, using 17 psychopathological variables, developed 88 archetypal psychiatric patients to represent four diagnostic categories (manic-depressive depressed, manic-depressive manic, simple schizophrenic, and paranoid schizophrenic). Ten computerized methods representative of the major clustering approaches and using various measures of similarity between patients were applied to this data set to develop de novo patient groupings. Evaluative criteria included the concordance of clustering output to the structure of the original data, and clustering replicability. Considerable differences were obtained among clustering methods. The best-ranked procedures were nearest centroid sorting methods and complete and centroid linkage hierarchical methods. The overall poorest ranking were obtained for multivariate normal mixture analysis and facial representation of multidimensional points. Further evaluation of cluster analytic methods on real biological and psychosocial data sets yielded similar rankings.

Bipolar Disorder↗
PubMed1978

Enlarged acid-base and blood gas calculations by electronical data computing in the blood gas laboratory.

A rapid anaysis of parameters of the acid-base equilibrium and blood gases during open heart surgery and emergency therapy is absolutely necessary. Computing of the several parameters of the acid-base status by slide rules or nomograms is time consuming and can be shortened by computer applications. The central blood gas laboratory consists of a blood gas analyzer for PO2, PCO2 and pH, an electronic desktop calculator, a four color X-Y-plotter and two data lines to the cardiac surgery unit and to the intensive care unit. The time needed for computing and feedback of the parameters could be decreased to one quarter. In addition to numerical data printout, a graphical representation of the several parameters is possible on a X-Y-plotter and includes the Rahn-Fenn-O2-CO2-Diagram with venous admixture, ventilation perfusion ratio, alveolar dead space ventilation and the standard and actual oxygen dissociation curve as well as the pH/HCO3- Acid-Base nomogram. Furthermore, a computer diagnosis of the actual disturbances can be plotted.

Acid-Base Equilibrium↗
PubMed1975

[Computer experience and further developments in the respiratory function laboratory (author's transl)].

Reported is on satisfactory results obtained with a small-size computer consisting of punching and scanning device, as well as plain writing machine in the respiratory function laboratory. Developed in on- as well as off-line processing by an own technical staff, a diagnostic and teaching program was established for all respiratory function routine methods with the advantages of a large number of cases examined, elimination of sources of error, considerable supply of data and information, automatic documentation and filing, plain writing, interpretation and evaluation of findings. In continuation of such works also the blood gas analysis has been included. These values as the total of disturbances of the pathophysiological acid-base status are considered and interpreted. Clinical correction is forced in this man-machine dialogue by automatic stops of the whole machinery before going on. Subsequently and in addition are computer alveolar-arterial oxygen pressure gradient, venous shunt and oxygen saturation and expressed utilizing the capacity of the small-size computer. Further developments in the respiratory function diagnostic- and teaching program for small-size computers--not too expensive in the building block principle - are intended.

Acid-Base Equilibrium↗
PubMed2026

Automated segmentation and length measurement of metacarpal and phalangeal bones for hand radiograph evaluation.

Evaluating hand and wrist radiographs is essential in pediatric endocrinology and clinical genetics, particularly for the assessment of suspected skeletal anomalies. In this study, we present Auto-Bone-Caliper, an automated system for the segmentation and length measurement of metacarpal and phalangeal (M&P) bones, trained and evaluated on public datasets comprising both normal and dysmorphic cases. We first introduce InstanceSAM, a two-stage framework that detects and segments all 19 M&P bones in pediatric hand radiographs, achieving Dice scores of 98.7% for normal bones and 95.0% for dysmorphic bones. We further develop and evaluate three methods for bone-length estimation, identifying a k-means-based approach as the most accurate, with relative errors of 2.2% for normal bones and 4.5% for dysmorphic bones. Our automated pipeline, Auto-Bone-Caliper, integrates InstanceSAM with the k-means-based length-estimation method. To enable scale-independent downstream analyses, we derive relative bone-length measures from the automated measurements. Using these relative measures, we statistically compare measurements obtained using Auto-Bone-Caliper on an independent dataset with a healthy reference catalog of normal bone morphologies, observing a high level of agreement (Wasserstein-1 distance = 0.012). Finally, we demonstrate a potential clinical use case of Auto-Bone-Caliper by obtaining relative metacarpophalangeal pattern profiles for three genetic conditions, namely Turner syndrome, achondroplasia, and pseudohypoparathyroidism. Our results highlight the potential of the Auto-Bone-Caliper to streamline and standardize M&P length measurement, providing an objective and reproducible tool suitable for clinical application.

Humans↗
PubMed2026

Hypernetwork-guided fusion with intra-class MixUp for breast cancer subtyping.

Accurate breast cancer subtyping guides treatment selection, yet histopathology captures morphology without molecular state, while genomic profiling captures molecular signatures without spatial context. Existing fusion methods rely on concatenation, or on attention applied only after each modality is encoded independently. This work identifies a scale-dependent asymmetry in the direction of cross-modal conditioning: the direction that performs best under limited samples is not the one that holds at scale, and the reversal is traced to the capacity of the modulation pathway rather than to the fusion principle. The comparison is carried out within a hypernetwork-guided framework in which an auxiliary network maps one modality to conditioning parameters that modulate the other's feature representation, shaping features at the parametric level rather than the decision stage; modulation is patient-specific rather than patch-specific. Both directions are instantiated-gene-to-image (HyperG2I) and image-to-gene (HyperI2G) - and trained under a label-aware MixUp strategy that interpolates within-class samples across both modalities, preserving the hard binary labels clinical decisions require. The framework is evaluated on two paired TCGA-BRCA cohorts-one limited-sample, one independently assembled at scale-under a single protocol spanning two whole-slide representations, multiple visual backbones, and both conditioning directions. On the limited-sample cohort, gene-to-image conditioning at its optimal augmentation setting exceeds early fusion and both unimodal baselines, giving the highest recall on the aggressive Basal/HER2 class of any configuration evaluated, and an ablation favours intra-class over inter-class mixing. At scale this ordering does not hold: image-to-gene conditioning sustains its performance whereas gene-to-image does not, recovering only partially under the full tissue bag and isolating the capacity of the modulation pathway as the binding constraint. Direction and capacity of cross-modal conditioning, rather than fusion depth alone, therefore govern how such frameworks scale.

Breast Neoplasms↗
PubMed2025

Cardiac CT fractal analysis of LV noncompaction and common cardiomyopathies.

BACKGROUND: Left ventricular noncompaction (LVNC), or hypertrabeculation, is a myocardial condition that remains challenging to diagnose and differentiate from other cardiomyopathies. This study evaluated the ability of cardiac CT to differentiate between LVNC, hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), and controls using fractal analysis of LV trabeculae. METHODS: Subjects with LVNC, HCM, DCM, as well as controls, who underwent coronary CT angiography were included. LV trabecular structure was quantified using fractal analysis on a stack of 15 short-axis CT images. For each subject, maximum (FDmax) and average (FDglobal) fractal dimensions were reported. A subset of subjects also had clinically acquired cardiac MRI (CMR) exams for comparison. One-way ANOVA, Pearson correlation, and Bland-Altman analysis were used for statistical analysis. RESULTS: The study included 313 subjects (median age: 58.8 [48.1-68.0] years, 153 male) categorized into Control (89), LVNC (46), HCM (106), and DCM (72) cohorts. FDmax was significantly higher in LVNC (1.379 &#x200b;&#xb1; &#x200b;0.047) than in Control (1.305 &#x200b;&#xb1; &#x200b;0.033), HCM (1.321 &#x200b;&#xb1; &#x200b;0.040), and DCM (1.344 &#x200b;&#xb1; &#x200b;0.054) cohorts; all p &#x200b;< &#x200b;0.001. Similarly, FDglobal was significantly higher in LVNC (1.279 &#x200b;&#xb1; &#x200b;0.041) than in the other cohorts; all p &#x200b;< &#x200b;0.05. In a subset of 132 subjects with both CT and CMR exams, fractal dimensions from the two modalities were strongly correlated (r &#x200b;= &#x200b;0.63, p &#x200b;< &#x200b;0.0001), with CT-derived values being higher (1.337 &#x200b;&#xb1; &#x200b;0.049 vs. 1.262 &#x200b;&#xb1; &#x200b;0.045, p &#x200b;< &#x200b;0.0001). CONCLUSIONS: CT-derived fractal dimensions of LV trabecular structure were significantly higher in LVNC compared to control subjects, HCM, and DCM. CT-derived fractal dimensions strongly correlated with, but were higher than, those from cardiac MRI in the same subjects.

Humans↗