Background: Postural instability and gait difficulties are key symptoms of Parkinson's disease (PD), elevating the risk of falls substantially. Falls afflict 35% to 90% of PD patients, representing a major challenge in managing the condition. Accurate prediction of fall risk and identification of contributing factors are essential for timely interventions.Objectives: Our objective was to develop and validate a machine learning (ML) algorithm across multiple centers in Italy to accurately forecast fall risk and identify related factors using routinely collected clinical data.Methods: Patient data from two Italian centers (N = 251) were divided into a training cohort (N = 164) for ML model development and a validation cohort (N = 87). External validation was conducted on a subset of PPMI study patients (N = 65). We compared the performance of logistic regression (LR) and Support Vector Classifier (SVC) models trained on clinical data. The Shapley Additive exPlanations (SHAP) method was employed to examine the predictive power of individual variables.Results: In the training set, SVC outperformed LR slightly (AUC: LR = 0.779 ± 0.054, SVC = 0.792 ± 0.056). However, LR demonstrated better prediction accuracy in both internal (AUC: LR = 0.753, SVC = 0.733) and external validation cohorts (AUC: LR = 0.714, SVC = 0.676). SHAP analysis on the LR model revealed associations between fall risk and both motor and non‐motor variables.Conclusions: ML‐based models effectively estimate fall risk across different clinical centers, enabling tailored interventions to enhance PD patients' quality of life. Challenges persist in predicting falls in US‐based patients due to demographic and healthcare system differences.

The positron emission tomography (PET) radiotracer [¹¹C]PBR28 has been increasingly used to image the translocator protein (TSPO) as a marker of neuroinflammation in a variety of brain disorders. Interrelatedly, similar clinical populations can also exhibit altered brain perfusion, as has been shown using arterial spin labelling in magnetic resonance imaging (MRI) studies. Hence, an unsolved debate has revolved around whether changes in perfusion could alter delivery, uptake, or washout of the radiotracer [¹¹C]PBR28, and thereby influence outcome measures that affect interpretation of TSPO upregulation. In this simultaneous PET/MRI study, we demonstrate that [¹¹C]PBR28 signal elevations in chronic low back pain patients are not accompanied, in the same regions, by increases in cerebral blood flow (CBF) compared to healthy controls, and that areas of marginal hypoperfusion are not accompanied by decreases in [¹¹C]PBR28 signal. In non-human primates, we show that hypercapnia-induced increases in CBF during radiotracer delivery or washout do not alter [¹¹C]PBR28 outcome measures. The combined results from two methodologically distinct experiments provide support from human data and direct experimental evidence from non-human primates that changes in CBF do not influence outcome measures reported by [¹¹C]PBR28 PET imaging studies and corresponding interpretations of the biological meaning of TSPO upregulation.

Background/Objectives: Neovascular age-related macular degeneration (nAMD) is a retinal disorder leading to irreversible central vision loss. The pro-re-nata (PRN) treatment for nAMD involves frequent intravitreal injections of anti-VEGF medications, placing a burden on patients and healthcare systems. Predicting injections needs at each monitoring session could optimize treatment outcomes and reduce unnecessary interventions. Methods: To achieve these aims, machine learning (ML) models were evaluated using different combinations of clinical variables, including retinal thickness and volume, best-corrected visual acuity, and features derived from macular optical coherence tomography (OCT). A “Leave Some Subjects Out” (LSSO) nested cross-validation approach ensured robust evaluation. Moreover, the SHapley Additive exPlanations (SHAP) analysis was employed to quantify the contribution of each feature to model predictions. Results: Results demonstrated that models incorporating both structural and functional features achieved high classification accuracy in predicting injection necessity (AUC = 0.747 ± 0.046, MCC = 0.541 ± 0.073). Moreover, the explainability analysis identified as key predictors both subretinal and intraretinal fluid, alongside central retinal thickness. Conclusions: These findings suggest that session-by-session prediction of injection needs in nAMD patients is feasible, even without processing the entire OCT image. The proposed ML framework has the potential to be integrated into routine clinical workflows, thereby optimizing nAMD therapeutic management.

Background: Synthetic data generation is emerging as an approach to overcome the limitations of real-world data scarcity in omics studies, especially in precision medicine and oncology. Omics datasets, with their high dimensionality and relatively small sample sizes, often lead to overfitting, especially in deep learning models. Generative models offer a promising way to generate realistic synthetic data preserving the original data distribution. However, there is still no objective consensus on how to evaluate their performance. In this study, we set out to validate generative networks for transcriptomics data generation by using statistical distances as robust evaluation metrics. Results: We observe that statistical distances enable simultaneous evaluation of global and local data fidelity of generated synthetic data. Because these distances satisfy the properties of true metrics, they also enable formal hypothesis testing to assess whether generative models have in fact converged or are merely approaching the reference distribution. Crucially, optimizing for these distances was found to implicitly select models maximizing other widely used metrics of generative performance, providing evidence of their broad applicability. Overall, our findings indicate that the adoption of these metrics can play a key role in guiding the development of generative models across a wide range of domains.

Diabetic retinopathy (DR) screening commonly relies on fixed follow-up intervals, although progression risk differs across patients. We developed a preliminary image–clinical framework to support personalized follow-up recommendations from retinal fundus images and systemic risk factors. Public DR datasets were harmonized into a binary task distinguishing absence of DR from DR of any grade. An ImageNet-pretrained ResNet50 and a foundation model-based feature extraction pipeline were compared. The selected image model was integrated with literature-derived severe retinopathy progression curves and clinical risk modifiers to estimate personalized cumulative risk and assign follow-up intervals using a predefined acceptable risk threshold. The ResNet50-based model was selected for subsequent analyses. In the target cohort, the integrated model assigned 90.0% of patients to follow-up within 12 months. Clinical adjustment substantially modified image-only recommendations, generally shifting patients toward shorter intervals. These preliminary findings support the feasibility of combining image-derived estimates of baseline DR status with clinical modifiers to inform personalized screening intervals. Larger longitudinal studies are needed to validate calibration, clinical utility, and safety before real-world implementation.