2026-10-20

Rationale And Objectives: Microvascular invasion (MVI) is a key factor associated with high postoperative recurrence rate in patients with hepatocellular carcinoma (HCC). This study aims to evaluate the value of super-resolution (SR) reconstruction based on hepatobiliary phase (HBP) MR images for improving the accuracy of MVI prediction. Materials And Methods: A total of 544 patients with HCC from two medical centers between 2018 and 2025 were retrospectively analyzed, including 450 patients in the training set, 51 in the internal validation set, and 43 in the external test set. Based on a lesion-aware 3D Restormer transformer architecture, SR reconstruction was performed using HBP MR images to generate super-resolution MR images. Tumor segmentations were automatically performed using the nnU-Net architecture and manually corrected by an experienced abdominal radiologist before radiomics feature extraction. Radiomics features were then extracted from original and SR images. Radiomics models based on original and SR images were developed to predict MVI status. Three machine learning algorithms, including Logistic Regression, Random Forest, and LightGBM, were used for model development. Results: A total of 851 radiomics features were extracted from the original and SR images, respectively. Redundant features were filtered, and 50 radiomics features were selected using LASSO regression with cross-validated. For MVI prediction, the pooled areas under the receiver operating curves (AUCs) of radiomics model based on the original images were 0.710, 0.764, and 0.614 in the training, validation and test sets, respectively. The corresponding AUCs of the radiomics model based on the SR images were 0.855, 0.798, and 0.827, respectively. In the test set, the AUC of the radiomics model based on the SR images was higher than that based on the original images (0.827 vs. 0.614, p <0.001). Conclusion: SR reconstruction can improve the diagnostic performance of radiomics models for predicting MVI in patients with HCC.

Background: Respiratory microbiome profiles contain compositional information that may complement clinical variables for mortality classification in hospitalized patients with COVID-19. However, microbiome-based classifiers remain vulnerable to compositional artifacts, patient-level leakage, and cohort-specific feature construction. Objective: To develop and externally evaluate a leakage-controlled sparse log-ratio learning framework for mortality classification using respiratory microbiome profiles. Methods: This retrospective secondary analysis used three public COVID-19 respiratory microbiome cohorts. The Sparse Log-Ratio Risk Network (SLRRN) was developed using patient-level nested validation, with genus filtering, zero replacement, balance construction, model tuning, and threshold selection restricted to training partitions. The model learned 16 signed genus balances and integrated four clinical covariates available at the first eligible respiratory sampling landmark. The final specification and threshold were frozen before external evaluation. Results: The development cohort included 82 patients (20 deaths) from PRJNA678105 and PRJNA683617, and frozen external evaluation included 76 independent patients (38 fatal and 38 severe nonfatal cases) from PRJNA726205. Internal AUROC was 0.838 (95% CI, 0.765-0.911), and AUPRC was 0.674 (0.577-0.771). The paired AUROC difference versus XGBoost was 0.041 (0.009-0.073). Without refitting or threshold revision, external AUROC was 0.821 (0.738-0.904), with sensitivity of 0.816 and specificity of 0.842 at the frozen threshold of 0.37. In the outcome-enriched external cohort, the Brier score was 0.166 and the ECE was 0.065; these estimates describe the selected study sample. Conclusion: SLRRN provides a composition-aware and leakage-controlled framework for respiratory microbiome-based mortality classification. The findings support further prospective evaluation with standardized sampling and direct comparison with established clinical models before clinical translation.

Objectives: Real-world implementation of large language models such as ChatGPT to enhance patient education remains limited. We evaluated the feasibility, acceptability and attitudes towards a ChatGPT-based counselling tool among patients and healthcare workers (HCWs) in a specialist infectious diseases clinic. Methods: We conducted a prospective clinical implementation study in a specialist HIV and sexual health clinic in Singapore from Jan 2025 to December 2025. Patients interacted with a custom ChatGPT-based chatbot prior to routine counselling by physicians or pharmacists. Responses were reviewed with patients subsequently during consultation. Post-interaction, patients and HCWs completed structured surveys incorporating domains from a validated survey tool: perceived risk, anxiety, technology/social influence and intention for future use. Results: The chatbot was most frequently used for counselling on antiretroviral therapy initiation or switching, and sexual health advice. A total of 35 patients and 35 HCW assessments were analysed. Patients had greater perceived risk with the use of the chatbot compared to HCWs, as indicated by lower median scores (13, IQR 10-16; vs. 18, IQR 15-21), while anxiety scores were similar. Technology/social influence scores were high across both groups (overall median 20, IQR 19-22). Most participants indicated willingness for future use. Qualitative feedback highlighted improved patient understanding and more focused consultations, but concerns included lack of contextual nuance and occasional inaccuracies. Conclusions: The pilot implementation of a ChatGPT-based counselling tool in a specialist HIV and sexual health clinic was feasible and generally acceptable to patients and HCWs. Further studies are required to evaluate accuracy and clinical effectiveness before broader implementation.

Early autism screening is essential for timely intervention but remains constrained by specialist dependence, costly protocols, and limited accessibility. We present Autism-CLIP, a vision-language model that automates screening from 3-min naturalistic caregiver-child interaction videos recorded in toddlers aged 15-24 months. By integrating contrastive learning, large language model-generated supervision, domain-specific architecture, and hybrid training, Autism-CLIP aligns visual behavioral cues with autism-related assessment outcomes, enabling annotation-free inference. In multicenter evaluation, Autism-CLIP achieved an AUC of 0.903 in the internal cohort (n = 103) and 0.873 in the external cohort (n = 102), outperforming conventional machine learning methods. Attention mapping highlighted clinically relevant markers, including gaze aversion and limited social reciprocity. Requiring only smartphone-captured videos without manual coding or controlled settings, Autism-CLIP offers a scalable, low-cost approach to broaden access to early autism screening and support prioritization of clinical resources.

Background: Facial appearance offers a non-invasive window into health. We built Foundation Artificial Intelligence for Health Recognition-Face (FAHR-Face), a foundation model trained on more than 40 million facial photographs and fine-tuned it for two distinct tasks: biological age estimation (FAHR-FaceAge) and survival prediction (FAHR-FaceSurvival). Methods: FAHR-FaceAge underwent two-stage, age-balanced fine-tuning on ten public image datasets; FAHR-FaceSurvival was fine-tuned on 34 363 photographs of patients with cancer. Model robustness (cosmetic surgery, make-up, pose, and lighting) and independence (attention mapping) were tested. Both models were clinically tested in two independent cancer patient datasets with survival analysed by multivariate Cox models and adjusted for clinical prognostic factors. Findings: For age estimation, FAHR-FaceAge had the lowest mean absolute error of 5·1 years on public datasets, outperforming benchmark models and maintaining accuracy across the full human lifespan. In patients with cancer, FAHR-FaceAge outperformed a previous age estimation model in survival prognostication. FAHR-FaceSurvival showed robust prediction of mortality, and the highest-risk quartile had more than triple the mortality of the lowest (adjusted hazard ratio 3·23; p<0·0001). These findings were validated in the independent cohort and both models showed generalisability across age, sex, race, and cancer subgroups. The two algorithms provided distinct, complementary prognostic information; attention mapping revealed that each model relied on distinct facial regions. The combination of FAHR-FaceAge and FAHR-FaceSurvival improved prognostic accuracy. Interpretation: A single foundation model can generate inexpensive, scalable facial biomarkers that capture both biological ageing and disease-related mortality risk. The foundation model enabled effective training by use of relatively small clinical datasets. Routine quantitative facial analysis could enhance risk stratification when traditional biomarkers are unavailable. Funding: EU European Research Council.