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PubMed2026

Role of Polygenic Risk Scores in Predicting Cognitive Functioning after Mild Traumatic Brain Injury: A TRACK-TBI Study.

Patients with traumatic brain injury (TBI) and Glasgow Coma Scale scores of 13-15 (historically called mild TBI [mTBI]) commonly experience changes in cognitive functioning, including processing speed, memory, and executive functioning. In a prospective sample (N = 523) of individuals of European descent who had been treated in a U.S. level 1 trauma center for mTBI, we examined the prognostic value of four polygenic risk scores (PRS) for cognitive outcomes at 6-months postinjury. To estimate the impact of mTBI on cognition, primary cognitive outcomes were scaled as z-scores reflecting changes in performance relative to predicted preinjury performance. The PRS examined were previously developed and validated to predict cognition-related outcomes of educational attainment (Education-PRS), intelligence (Intelligence-PRS), and Alzheimer's disease (AD-mild traumatic brain injury (APOE)-PRS and AD + APOE-PRS). Both the Education-PRS and Intelligence-PRS displayed bivariate associations with all four cognitive outcomes (β = 0.19-0.32), whereas neither Alzheimer's disease PRS was significantly associated with any outcome. After controlling for other factors known to predict cognitive outcomes of TBI (e.g., sex, education, mTBI severity defined by a combination of Glasgow Coma Scale scores and the presence/absence of acute intracranial findings on clinical neuroimaging), the Education-PRS and Intelligence-PRS remained independently predictive of verbal episodic memory (β = 0.10-0.16), whereas their associations with processing speed and executive functioning were mostly nonsignificant and were mediated through educational attainment. Looking across primary z-score and secondary raw score outcomes, cognitive outcomes 6 months post-mTBI were good on average, and PRS made small independent contributions to outcome prediction. The mediation model findings may support theories of cognitive reserve, which propose that individuals with stronger preinjury cognitive processing abilities (often estimated by educational history) can better compensate for TBI. Moreover, findings indicate that PRS may contribute modestly to multivariable models predicting cognitive function after TBI.

Humans↗

PubMed2026

Imaging‑based models for predicting cerebrovascular complications of carotid stenosis.

This is a protocol for a Cochrane review (prognosis). The objectives are as follows: Primary objective To systematically review and critically appraise multivariable prognostic models developed for adults (≥ 18 years) with carotid stenosis in which imaging biomarkers (e.g. plaque characteristics derived from magnetic resonance imaging (MRI), computed tomography (CT), or ultrasound) constitute the core predictors. The primary focus is to evaluate the predictive performance of these models for cerebrovascular complications - specifically ipsilateral ischaemic stroke and transient ischaemic attack (TIA) - which are the clinical outcomes to be predicted. Where feasible, we will summarise and compare the models' discrimination (C‑statistic/area under the curve (AUC)) and calibration (calibration‑in‑the‑large, calibration slope, observed‑to‑expected ratio) across studies, and assess their potential for clinical application and external validation. For the purpose of defining symptomatic carotid stenosis as an eligibility criterion and subgroup variable, we will include studies that also considered retinal ischaemia (e.g. retinal embolism, amaurosis fugax) as a qualifying event. Secondary objectives To describe the combinations of imaging markers, modelling techniques, sample sizes, and variable‑selection strategies used in the development of the included models To evaluate the performance of these models for additional secondary clinical outcomes: plaque progression or regression, incident high‑risk imaging features, and the transition from asymptomatic to symptomatic disease To explore whether predictive performance differs according to imaging modality (MRI versus CT versus contrast‑enhanced ultrasound (CEUS)) or technical protocol (e.g. 3 T versus 1.5 T, spectral CT versus conventional CT) For studies that report both cerebrovascular and broader cardiovascular outcomes (major adverse cardiovascular events, myocardial infarction, etc.), we will only extract the performance metrics relating to cerebrovascular events for the primary analysis. Performance metrics for cardiovascular outcomes will be considered exploratory and will not form part of the main synthesis.

Humans↗

PubMed2026

Diagnostic performance of machine learning models for malignant and non-malignant pleural effusion: Systematic review and meta-analysis.

BACKGROUND: Accurately distinguishing malignant pleural effusion (MPE) from non-malignant pleural effusion is clinically important, but the generalisability and methodological quality of machine-learning (ML) models remain uncertain. METHODS: We searched eight databases to 23 April 2026. Diagnostic performance was pooled using random-effects and Reitsma bivariate models, and study quality was assessed using PROBAST+AI. RESULTS: Forty-two studies were included; 17 contributed to the AUC meta-analysis and 14 to the bivariate analysis. The pooled AUC was 0.90 (95 % CI 0.85-0.94; 95 % prediction interval 0.62-0.98), with sensitivity of 0.80 (95 % CI 0.77-0.83) and specificity of 0.87 (95 % CI 0.79-0.92). Only nine studies reported external, temporal or independent validation. Externally validated studies had a lower pooled AUC than studies without external validation (0.83 vs 0.92), with lower specificity observed in the two externally validated studies contributing sensitivity and specificity data. All 42 development assessments had high overall quality concerns, and all 42 model evaluations were judged at high risk of bias. CONCLUSIONS: ML models showed good apparent accuracy for distinguishing MPE from non-MPE, but the evidence was limited by substantial heterogeneity, high risk of bias and scarce external validation. The pooled estimates reflect the average performance of different selected models rather than the expected accuracy of a single clinical test. ML models should be regarded as adjuncts to existing diagnostic pathways until they are confirmed by rigorous multicentre prospective external validation and clinical-impact studies.

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PubMed2026

Distress Intolerance, Social Anxiety, and Depressive Symptoms in Adolescents: Evidence from Random-Intercept Cross-Lagged Panel and Cross-Lagged Panel Network Analyses.

Social anxiety and depressive symptoms frequently co-occur during adolescence, yet the mechanisms underlying their longitudinal associations remain insufficiently understood. Distress intolerance has been proposed as a transdiagnostic risk factor implicated across internalizing symptoms. However, it remains unclear whether distress intolerance is predicted by social anxiety and depressive symptoms, as well as the underlying mechanisms among these three constructs. The specific symptoms most centrally involved in these cross-domain associations remain poorly understood. The present study investigated the longitudinal associations among distress intolerance, social anxiety, and depressive symptoms using a dual-method framework combining random-intercept cross-lagged panel models (RI-CLPM) and cross-lagged panel network (CLPN) analyses. A total of 1,378 Chinese adolescents (Mage = 12.57, SDage = 0.63; 50.4% female) were assessed at three time points with six-month intervals between waves. The RI-CLPM analyses revealed that higher distress intolerance prospectively predicted subsequent increases in both social anxiety and depressive symptoms, whereas elevated social anxiety and depressive symptoms in turn predicted subsequent increases in distress intolerance. Moreover, distress intolerance mediated the longitudinal associations between social anxiety and depressive symptoms. Additionally, distress intolerance was also indirectly associated with its own subsequent levels through social anxiety and depressive symptoms. The CLPN analyses revealed that fear of negative evaluation and fatigue were the strongest predictors of other network nodes from T1 to T2 and from T2 to T3, respectively. In contrast, distress intolerance symptoms were predominantly predicted by other nodes in both cross-lagged networks. These findings extend prior views of distress intolerance as a unidirectional vulnerability by showing that distress intolerance is also predicted by social anxiety and depressive symptoms and accounts for part of their longitudinal associations across adolescence.

Humans↗

PubMed2026

Could the preoperative urethral curve be used to predict immediate urinary continence following Retzius-sparing robot-assisted radical prostatectomy? A retrospective multi-center study.

PURPOSE: Immediate urinary continence (UC) recovery following Retzius-sparing robot-assisted radical prostatectomy (RS-RARP) remains highly variable, highlighting the need for reliable preoperative prediction. We aimed to develop and validate models to identify patients likely to achieve immediate UC recovery following RS-RARP. MATERIALS AND METHODS: A total of 580 prostate cancer patients who underwent RS-RARP from four medical centers were assigned to a training set (n=348), an internal validation set (n=103) and an external validation set (n=129). Independent predictors were identified through univariate analysis and LASSO regression. A nomogram was constructed using multivariate logistic regression. Its performance was evaluated with receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis. RESULTS: Immediate UC recovery was observed in 84.5% (294/348) of patients in the training cohort, 80.6% (83/103) in the internal validation cohort, and 81.4% (105/129) in the external validation cohort, respectively. Multivariate analysis identified membranous urethral length (MUL) (OR=1.23, P=0.029) and urethral curvature (OR=2.84, P<0.001) as independent predictors, while prostate volume (PV) (OR=0.84, P <0.001) as a protective factor. The nomogram integrating MUL, PV, and urethral curvature demonstrated superior predictive accuracy, with an AUC of 0.87 (95% CI, 0.83-0.91) in the training cohort. The bootstrap-corrected calibration slope was 0.96, and the Brier score was 0.08. Calibration curves and decision curve analysis confirmed the predictive accuracy and clinical utility of the nomogram. CONCLUSIONS: Our study introduces a novel quantitative method for assessing urethral curvature. The mpMRI-based model, integrating urethral curvature and prostate spatial configuration, offers enhanced predictive accuracy for postoperative immediate UC recovery.

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PubMed2026

A Dynamic Nomogram to Predict Metabolic Dysfunction-Associated Fatty Liver Disease in Patients with Metabolic Syndrome.

BACKGROUND: Metabolic syndrome (MetS) involves multiple metabolic disorders. This study aimed to identify high-risk populations for metabolic dysfunction-associated fatty liver disease (MAFLD) in patients with MetS and to establish a dynamic predictive nomogram. METHODS: A total of 627 patients with MetS from six regions in Zhejiang Province were enrolled and categorized into MAFLD and non-MAFLD groups, then randomly assigned to training and validation sets at a ratio of 7:3. Independent predictors of MAFLD were identified using least absolute shrinkage and selection operator regression and multivariable logistic regression analyses. These predictors were then used to construct a dynamic nomogram. RESULTS: A total of 627 patients with MetS were included in the final analysis, of whom 77.0% (483/627) were diagnosed with MAFLD. Multivariable logistic regression analysis identified body mass index (BMI), waist circumference (WC), total cholesterol (TC), alanine aminotransferase (ALT), MetS-defined dysglycemia, and education level as independent risk factors for MAFLD. MetS-defined dysglycemia showed the highest odds ratio (OR) for MAFLD development [OR = 1.87, 95% confidence interval (CI): 1.07-3.29]. Although the number of MetS components and the metabolic syndrome score were significantly associated with MAFLD in univariate analysis, they were not independently associated with MAFLD in the multivariate model. A dynamic nomogram for predicting MAFLD risk in patients with MetS was developed and internally validated. The area under the receiver operating characteristic curve was 0.834 (95% CI: 0.787-0.880) in the training set and 0.839 (95% CI: 0.771-0.899) in the validation set, indicating strong predictive performance. Bootstrap internal validation demonstrated good agreement between predicted and observed outcomes in calibration curves. Decision curve analysis further indicated favorable clinical applicability of the nomogram. CONCLUSION: BMI, WC, TC, ALT, MetS-defined dysglycemia, and education level are independent risk factors for MAFLD. A dynamic nomogram for predicting MAFLD risk in patients with MetS was successfully developed and validated.

Humans↗

PubMed2026

Predictive evolutionary genomics: principles, validation, and practice.

Climate change and habitat loss are driving rapid evolutionary responses in populations world-wide, which creates an urgent need for evolutionary forecasting in conservation and agriculture. Such forecasting can be categorized into three time scales: trait-based models that use multivariate quantitative genetic equations to project correlated phenotypic responses up to c. 20 generations, allele-based analyses that model allele frequency dynamics up to 100 generations, and composite adaptation scores that aggregate many small effects to yield predictions across longer horizons. However, these approaches have remained largely disconnected. Here, we present a Bayesian framework that integrates these three complementary approaches for evolutionary prediction. Our framework combines genomic, phenotypic, and environmental data to yield probabilistic predictions with explicit uncertainty. We show how predictive evolutionary forecasts can be validated with experimental evolution, field experimentation, historical specimens, and reciprocal transplants. These validated forecasts can help advance conservation and agricultural programmes by helping predict which populations are at risk of future extinction, optimizing breeding programmes for future climates, and planning ecosystem management under environmental change. By supporting a shift towards more predictive approaches in evolutionary biology, this framework may help improve our ability to manage biodiversity and food security in a changing world.

Genomics↗

PubMed2026

Lateral Femoral Condyle Angle Is an Anatomic Risk Factor for Anterior Cruciate Ligament Primary Injury and Secondary Graft Rerupture.

PURPOSE: To define the lateral femoral condylar angle (LFCA)-a parameter measuring condylar inclination-and assess its predictive capacity for primary and recurrent anterior cruciate ligament (ACL) injuries. METHODS: Patients aged 18 to 50 years who underwent primary ACL reconstruction between 2017 and 2022 were screened. Those with graft rerupture were matched 1:1:1 by sex, age, body mass index, and meniscal status to ACL-intact controls and patients without graft failure during a minimum 48-month follow-up. LFCA was measured as the angle between the long axis of the distal femur and the axis of the lateral femoral condyle, alongside 38 established parameters using preoperative 3.0-T magnetic resonance imaging scans. Intergroup differences were assessed with one-way analysis of variance; multivariate regression and receiver operating characteristic analyses identified independent predictors and evaluated predictive performance. RESULTS: The study included 540 patients (180 per group: primary ACL injury, rerupture, controls). One-way analysis of variance revealed a significant gradient in LFCA across controls (81.3°, 95% CI: 80.8-81.8°), primary injuries (81.9°, 95% CI: 81.5-82.3°), and reruptures (84.2°, 95% CI: 83.7-84.6°) (P < .001). In multivariate models adjusted for baseline factors, LFCA independently predicted primary injury (odds ratio [OR] = 1.27, 95% CI: 1.13-1.42, P < .001; area under the curve [AUC] = .576) and rerupture (OR = 1.46, 95% CI: 1.21-1.76, P < .001; AUC = .704). Combined models incorporating LFCA with tibial slopes, notch angle, and notch height achieved an AUC of .847 for primary injury prediction, whereas models with LFCA, lateral condylar flexion curvature radius, slope asymmetry, and tibial AP distance reached an AUC of .817 for rerupture. LFCA alone with lateral condylar flexion curvature radius yielded an AUC of .777 for rerupture prediction. Sex-stratified analyses confirmed LFCA predicted rerupture in both male patients (OR = 1.44, 95% CI: 1.19-1.75) and female patients (OR = 1.49, 95% CI: 1.16-1.92), and primary injury in male patients (OR = 1.30, 95% CI: 1.15-1.48). CONCLUSIONS: LFCA is an independent predictor of ACL injury, showing particular strength in identifying Asian patients at high risk for graft rerupture. LEVEL OF EVIDENCE: Level III, retrospective case-control study.

Humans↗

PubMed2026

Machine learning-ready genomic biomarkers: ATF3 polymorphisms predict postoperative analgesic demand through AI-compatible phenotyping.

PURPOSE: To determine whether ATF3 polymorphisms can serve as genetic biomarkers for machine learning-based precision analgesia by establishing a genotype-phenotype association suitable for predictive modeling of postoperative opioid requirements. METHODS: In a prospective cohort of 167 adults undergoing abdominal surgery, ATF3 SNPs rs3122721 and rs3125293 were genotyped. A structured dataset architecture was developed to represent genetic profiles as input features for supervised learning models, enabling translational analysis of genotype‑dependent opioid consumption over 72 h. RESULTS: Patients with homozygous genotypes of the ATF3 SNPs had significantly higher opioid requirements than non‑carriers, despite reporting similar subjective pain scores. This consistent genotype‑dependent pattern provided a clinically relevant phenotype suitable for integration into predictive algorithms. CONCLUSION: ATF3 genotyping offers a promising biomarker for computationally informed precision analgesia. By linking genomic variability to clinically meaningful outcomes within a structured clinical and genomic framework, this approach supports the future development of risk-stratified clinical decision-support systems to optimize postoperative pain management.Trial registration ChiCTR1900021991, registered 30 April 2019. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at https://doi.org/10.1007/s13755-026-00480-9.

ATF3↗

PubMed2026

Factors associated with additional intervention requirement following ESWL in pediatric patients with urolithiasis.

OBJECTIVE: To identify predictors of additional intervention following extracorporeal shock wave lithotripsy (ESWL) in pediatric patients and to develop a clinically applicable predictive model. MATERIALS AND METHODS: This retrospective cohort study included 647 pediatric patients who underwent ESWL between 2015 and 2025. Demographic, clinical, and radiological variables were analyzed. Univariable and multivariable logistic regression analyses were performed to identify independent predictors of additional intervention. Model performance was evaluated using receiver operating characteristic curve analysis. RESULTS: Additional intervention was required in 65 patients (10.0%). On multivariable analysis, stone size 10-20 mm (OR: 3.04, p = 0.003), moderate (OR: 2.16, p = 0.049) and severe hydronephrosis (OR: 6.05, p < 0.001), and multiple stones (OR: 3.52, p = 0.030) were identified as independent risk factors. Increasing age (OR: 0.84, p = 0.026), history of urolithiasis (OR: 0.41, p = 0.006), and lower calyx location (OR: 0.14, p = 0.034) were associated with a reduced risk. The model demonstrated good discriminative performance (AUC: 0.794), with a sensitivity of 72% and specificity of 75%. Internal validation using bootstrap resampling demonstrated stable model performance, yielding a corrected AUC of 0.732. CONCLUSION: Stone burden, hydronephrosis severity, and stone multiplicity are key determinants of additional intervention after ESWL in pediatric patients. The proposed model shows good predictive performance and may support individualized risk stratification and clinical decision-making.

Humans↗

PubMed2026

Dosimetric Parameters of the Heart and Its Substructures in Predicting Cardiac Events or Survival in Patients With Lung Cancer After Radiation Therapy: A Systematic Review and Meta-analysis.

The predictive value of radiation dose to the whole heart (WH) and cardiac substructures (CS) for cardiac events (CEs) and survival in patients with lung cancer remains uncertain. The goal of this study was to conduct a systematic review and meta-analysis to provide an evidence-based estimate of the relationship between these associations. A systematic meta-analysis was performed following PRISMA guidelines. Risk of bias was assessed using the JBI Critical Appraisal Checklist for Case Series. Outcomes were classified into major adverse cardiac events (MACE), arrhythmias, pericardial effusion, and survival. Depending on heterogeneity, random- or fixed-effects models were applied to calculate pooled hazard ratios (HRs) for univariable and multivariable analyses. A total of 80 studies, including 21,645 patients, were analyzed. Of these, 25 studies reported CEs, and 69 reported survival outcomes. Among 91 WH and 215 CS parameters evaluated, several showed significant associations. Key findings from our meta-analysis include: (1) left anterior descending (LAD) V15 was significantly associated with MACE. The mean heart dose (MHD), as well as ventricle and LAD doses, were significantly associated with ischemic events. (2) Multiple CS parameters were associated with different arrhythmia subtypes. (3) MHD, heart V5/V35/V55 and pericardial doses were significantly associated with pericardial effusion. (4) MHD was significantly associated with survival; CS parameters also showed predictive value, and especially, heart base dose being the most significant. (5) We also identified several thresholds with potential predictive values, such as LAD V15 <10% for MACE, left pulmonary vein (LPV) V55 <2%, and right pulmonary vein (RPV) V10 <54% for atrial fibrillation (AF), right atrium (RA) V60 <0.03 cc for non-AF supraventricular tachyarrhythmia, and left main artery (LMA) V10 ≥1 cc for bradyarrhythmia. This study identified 130 WH and CS dosimetric parameters associated with CEs and 131 with survival outcomes. These findings enhance our understanding of radiation-induced heart injury mechanisms and provide guidance for potential protective and intervention strategies.

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PubMed2026

Novel non-contrast computed tomography parameters for predicting spontaneous stone passage and surgical requirement in ureteral stones: The role of ureteral wall thickness and dilatation ratio.

We investigated the predictive value of standard non-contrast computed tomography (NCCT) measurements, the ureteral dilatation ratio (DDR) and intraluminal urine stasis markers, for spontaneous stone passage (SSP) versus surgical intervention in patients with ureteral stones. We also evaluated ureteral wall thickness (UWT) as a practical clinical marker. This retrospective study included 461 patients diagnosed with ureteral stones via NCCT. Patients were categorised into two groups based on clinical outcomes: the spontaneous passage group (MET; n = 229) and the endoscopic surgery group (URS; n = 232). Stone volume, stone density (HU), UWT, DDR and intraluminal urine attenuation values were measured for all patients. Independent risk factors were identified using a multivariate logistic regression model and clinical cut-off values were determined via ROC curve analysis. Stone volume, density, UWT and hydronephrosis grade were all significantly higher in the URS group. Multivariate regression analysis revealed that increased UWT (OR: 5.03, 95% CI: 3.66-6.90; p < 0.001) was the strongest independent predictor of surgery. Higher DDR (OR: 1.88; p = 0.003), advanced hydronephrosis, stone volume, and density also increased surgical risk. A UWT cut-off ≥ 2.97 mm predicted surgery with 84.8% sensitivity and 84.3% specificity (AUC: 0.872). A DDR cut-off > 1.79 yielded 81.7% specificity and 40.4% sensitivity. UWT weakly correlated with stone volume (r = 0.145), indicating wall thickening reflects an inflammatory response rather than a mere mechanical consequence. UWT is a superior predictor of SSP failure, supported by increased DDR as a highly specific complementary risk factor. These parameters could help clinicians to identify patients who would benefit from early surgical counselling and intervention rather than prolonged conservative management.

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PubMed2026

A risk-need-responsivity (RNR)-informed systematic review of needs during the pretrial period.

OBJECTIVE: Pretrial risk assessments are becoming increasingly popular in the United States. Despite the importance of assessing and intervening around "needs" in the risk-need-responsivity model, few pretrial risk assessments include comprehensive assessment of needs. We aim to provide a systematic review of the prevalence of and predictive utility of needs within the pretrial population. HYPOTHESES: There were no hypotheses given the nature of the study. METHOD: We conducted searches for articles in the EBSCO, ProQuest, and Google Scholar databases using key words related to 11 needs domains: antisocial personality, procriminal attitudes, procriminal associates, substance use, family/marital relationships, school/work, prosocial recreational activities, self-esteem, housing, mental health, and physical health. We identified 215 articles that reported on the prevalence of needs or explored their predictive associations with pretrial misconduct outcomes in adult populations. RESULTS: Overall, we find few comprehensive investigations of needs in the pretrial domain, apart from substance use. Variation in methodology and operationalization contributes to wide variability in prevalence estimates. We found only 15 articles that examined predictive associations between pretrial needs and outcomes, which were limited to investigations of behavioral health, employment, and housing needs. Substance use and housing needs emerged as the only consistent predictors of pretrial misconduct. CONCLUSIONS: Researchers should more directly assess the prevalence and predictive utility of needs within the pretrial period to bolster the evidence base for including these factors in pretrial risk assessments. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

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PubMed2026

Applications of artificial intelligence in robot-assisted surgery: a systematic review.

To characterize applications of artificial intelligence (AI) in robot-assisted surgery, summarize technical and clinical performance, and assess the quality of the available evidence. PubMed, Web of Science Core Collection, and Scopus were searched for English-language journal articles published from 1 January 2020 through 31 October 2025. Randomized, observational, model-development, validation, and feasibility studies evaluating AI in robot-assisted surgery or closely related image-guided minimally invasive workflows were eligible. Two reviewers independently performed study selection, data extraction, and risk-of-bias assessment. Owing to heterogeneity in surgical procedures, AI tasks, analytical units, validation strategies, and outcomes, findings were synthesized descriptively without statistical pooling. The review was registered in the International Prospective Register of Systematic Reviews (CRD420251175699). Seventeen studies were included: seven clinical prediction or decision-support studies, eight intraoperative recognition, segmentation, or image-guided studies, and two training or workflow studies. Five prediction studies reported area-under-the-curve values of 0.74-0.95. Technical studies reported F1 or Dice scores of 0.525-0.995 and task-specific accuracies of 0.840-0.998. Two randomized studies suggested benefits for personalized suturing feedback and automated camera control, but neither established improved patient outcomes. Only one study had low overall risk of bias; the remaining studies were at high or unclear risk or raised some concerns. AI applications in robot-assisted surgery show promise for prediction, intraoperative perception, training, and workflow support. Evidence primarily demonstrates technical feasibility rather than established clinical effectiveness. Independent multicenter validation and prospective evaluation of patient, educational, and workflow outcomes are required before widespread implementation.

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PubMed2026

The role of artificial intelligence in the diagnosis and prognosis of traumatic brain injury based on brain CT scans: a systematic review.

Traumatic brain injury (TBI) is a leading cause of emergency department visits and a major contributor to injury-related mortality and long-term neurological disability. Non-contrast computed tomography (CT) is the gold-standard imaging modality for the rapid diagnosis of TBI. Clinical outcomes depend strongly on early detection and prompt acute management. Artificial intelligence (AI)-based models may support faster automated identification of traumatic findings and early prediction of patient prognosis. A systematic literature search was conducted in PubMed/MEDLINE, Scopus, IEEE Xplore, ACM Digital Library, and the Cochrane Library in accordance with PRISMA 2020 guidelines to evaluate AI-based models for automated detection of TBI-related findings on CT and for prediction of clinical outcomes. Risk of bias and applicability were assessed using QUADAS-2 for diagnostic accuracy studies and PROBAST + AI for prediction model studies. Twenty-two studies were included. Sixteen studies evaluated diagnostic tasks and 10 evaluated prognostic outcomes, with four studies contributing to both categories. Diagnostic performance was generally high, with many studies reporting AUC values approaching or exceeding 0.90, particularly for larger lesion volumes.Prognostic performance was more variable, with moderate to high discrimination and substantial heterogeneity. Only 9 studies incorporated independent external validation, and performance was frequently lower in external cohorts. All prognostic model studies were judged to be at high overall risk of bias using PROBAST + AI, and most diagnostic accuracy studies also demonstrated high or unclear risk of bias in at least one QUADAS-2 domain, most frequently in patient selection. AI-based models applied to brain CT demonstrate strong technical performance for both diagnostic and prognostic tasks in TBI. However, most studies relied on retrospective designs and lacked independent external validation which limits models generalizability and raises concern for potential overfitting. Prospective, multicenter studies with standardized methodologies and rigorous external validation are required before widespread clinical implementation.

Humans↗

PubMed2026

An oxidative stress - and immunotherapy-related six-gene signature defines immune subtypes and predicts prognosis and immunotherapy response in hepatocellular carcinoma.

BACKGROUND: Oxidative stress and the tumor immune microenvironment jointly shape hepatocellular carcinoma (HCC) progression and response to immunotherapy, yet integrated biomarkers linking these processes are lacking. METHODS: Transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were used to identify oxidative stress- and immunotherapyrelated differentially expressed genes (OSIRDEGs). Functional enrichment, weighted gene co-expression network analysis (WGCNA) and LASSO-Cox regression were used to construct a prognostic signature. Consensus clustering, TIDE, CIBERSORT and ssGSEA characterized immune phenotypes. Somatic mutation, copy-number and drug-response data were integrated to assess genomic alterations and drug sensitivity. Expression of model genes was validated by qRT-PCR and western blotting in HCC cell lines. RESULTS: We identified 24 OSIRDEGs enriched in cell-cycle and mitotic pathways. WGCNA intersection yielded 18 module genes, from which a six-gene signature (BUB1B, CDKN2A, CENPE, HMMR, PTTG1, SPP1) was derived. The signature robustly stratified patients into high- and low-risk groups with significantly different progression-free and disease-free survival in both TCGA-LIHC and GSE14520. Based on signature expression, two molecular subtypes were defined, exhibiting distinct survival, immune landscapes and predicted immunotherapy responsiveness. Model genes harbored recurrent alterations and showed significant correlations with anticancer agents. All six genes were upregulated at mRNA and protein levels in metastatic HCC cell lines versus normal hepatocytes. CONCLUSIONS: We systematically explored the landscape of OSIRDEGs in HCC, and proposed a validated six-gene signature that refines prognostic stratification, delineates immunerelevant HCC subtypes and highlights candidate biomarkers for therapeutic selection and mechanistic investigation.

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PubMed2026

Artificial intelligence in treatment prediction for skeletal Class III malocclusion: A systematic review.

In skeletal Class III patients, treatment options range from orthodontics to orthognathic surgery. Choosing the optimal approach requires a comprehensive clinical evaluation, which may be supported by AI tools. The aim of this study was to assess the performance of AI models in predicting the need for orthognathic surgery and in identifying predictors influencing treatment decisions. A PRISMA-guided electronic database search (PubMed, Web of Science; 2009-2024; English/French) was performed to identify studies using machine learning (ML) or deep learning (DL) on cephalometric and clinical data. After screening and assessment for eligibility, 15 studies were critically appraised. Model performance was summarized using accuracy, sensitivity, specificity, and the area under the curve (AUC). ML algorithms (particularly Random Forest and XGBoost) and DL models (ResNet-based convolutional neural networks (CNNs)) achieved high accuracy for predicting surgical need. Frequently selected predictors included Wits appraisal, ANB angle, the maxillomandibular ratio (Mx/Md), overjet, and the divergence of the lower gonial angle. AI methods show promise for assisting treatment decisions in Class III malocclusion, with Random Forest and XGBoost performing well on tabular cephalometric data and CNNs on imaging. Larger, multicentre datasets and external validation are needed to improve reliability, address bias, and support clinical implementation.

Humans↗

PubMed2026

Longitudinal Prediction of Retinal Sensitivity Based on Disease Progression Quantified From Optical Coherence Tomography in Geographic Atrophy.

PURPOSE: The purpose of this study was to analyze the association between disease progression of geographic atrophy (GA) from optical coherence tomography (OCT) with retinal sensitivity (RS) in microperimetry (MP) over a 2-year follow-up period. METHODS: This is a longitudinal analysis of the OAKS Phase-III clinical trial. Both study and fellow eyes with GA that underwent imaging with the Spectralis OCT and consecutive MP examination were eligible. Pointwise quantification of ellipsoid zone (EZ) thickness, EZ and retinal pigment epithelium (RPE) loss from OCT volumes was correlated with localized RS. A longitudinal predictive model using a Markov Chain framework was implemented to predict RS change over time based on OCT biomarkers. The modeling of morphological and functional progression was based on the fellow-eye cohort. RESULTS: A total of 39,681 MP points from 406 patients were analyzed. In the fellow eye cohort, baseline (BSL) EZ thickness was positively associated with RS (0.3 decibel [dB]/µm, P < 0.001). Decrease in EZ thickness between visits during follow-up was significantly associated with decrease in RS (0.1 dB / 1 µm change). RS was significantly lower in MP points within EZ loss during follow-up compared with MP points within the retina with measurable EZ (P < 0.001). The largest functional decline was observed within RPE loss, also associated with the highest probability of absolute scotoma (P < 0.001). Morphological progression to EZ and RPE loss was influenced by EZ thickness and the morphology of adjacent MP points (P < 0.001). CONCLUSIONS: Two exploratory endpoints were developed, namely quantification of EZ thickness and loss, and localized RS within high-risk OCT areas. RS decline during follow-up is associated with automatically quantified disease progression in OCT.

Humans↗