Antonios Danelakis

Objectives: We aimed to forecast headache in individuals with persisting postconcussion symptoms using foundation machine learning (ML) models and multimodal longitudinal data. Methods: This was an ML analysis of data from the Digital Solutions for Concussion (DiSCo) study, a research project assessing the usability and feasibility of 2 mobile health apps for individuals with persisting postconcussion symptoms. The participants completed daily symptom registrations and daily biofeedback sessions measuring heart rate variability, peripheral skin temperature, and upper trapezius muscle tension. Two foundation ML models, TabPFN and longitudinal TabPFN, were recruited and evaluated to predict moderate-to-severe (at least 4 on the 11-point numeric rating scale) headache days occurring in the subsequent 24-hour window using prior physiological and symptom data. The top-performing model was further evaluated for forecasting days with fatigue, which was rated as the second to headache most bothersome symptom, within the subsequent 24-hour window. Models were trained and optimized on a train-set and evaluated on a hold-out test-set with the area under the receiver operating characteristics curve (AUC) and 95% confidence intervals (CIs). Results: Twenty individuals were included in the forecasting models, with data collected over a planned 28-day registration period, yielding a total of 338 days. The most stable model, longitudinal TabPFN, achieved a test-set AUC of 0.69 (95% CI: 0.62–0.74). The conventional TabPFN model achieved an AUC of 0.68 (95% CI: 0.55–0.80). In the case of fatigue, the longitudinal TabPFN model achieved an AUC of 0.81 (95% CI: 0.75–0.86). Both physiological measurements and symptomology information seemed to be important predictors. Conclusions: Moderate-to-severe postconcussion headache days can be predicted with modest accuracy 24 hours before their occurrence from multimodal physiological and self-reported symptomatology data. Incorporating temporal dynamics using longitudinal foundation models may improve such forecasting.

Migraine is highly heritable, yet no machine learning models for predicting treatment responses have incorporated genetic data. We aimed to develop machine learning models including both genetic and clinical information to predict treatment responses of migraine preventives and treatment retention of triptans. This was a cross-sectional population-based machine learning analysis of the Trøndelag Health Studies and the national prescription registry. Clinical predictors included demographics, serum metabolic measures, comorbidities, mental health and lifestyle factors. Genetic predictors included 108 migraine risk loci. Preventive response was defined as ≥50% reduction in triptan dispensations after first preventive dispensation, and triptan retention as ≥3 dispensations. The dataset was split into training (70%), validation (10%) and test set (20%). A series of standard and causal machine learning architectures were trained and optimized on the training and validation set, and the best model was evaluated on the held-out test data. Shapley Additive exPlanation values identified key predictors. In total, 475 participants tried a preventive (amitriptyline, beta-blocker, candesartan or topiramate) and 565 a triptan (371 sumatriptan) during the project period. Using only clinical features, the best models achieved area under curve of 0.56-0.69 for the preventives, and 0.56 for triptans. When combining clinical and genetic data, the area under curve was unchanged or reduced in all cases except for amitriptyline (0.80) and sumatriptan (0.55). The most important predictors were hormone-related features, lipid levels and hemodynamic parameters. We developed models that predicted treatment responses to amitriptyline, beta-blockers, candesartan and topiramate with modest accuracy. Incorporating genetic data did not enhance performance, although the limited sample size and the uncertain accuracy of cohort definition and treatment response assessment prevent firm conclusions.

Background and ObjectivesIn the absence of biomarkers, the true biological footprint of migraine remains incompletely understood. It could perhaps be best characterized using machine learning model...

Clinical use of AI requires verifiable trust in governance, fairness, privacy, and robustness. Federated machine learning (FML) changes how models are trained and audited and must be assessed in clinical settings. This work presents a sociotechnical governance study of FML on unstructured clinical notes across two Nordic healthcare sites. Using the Z-Inspection® methodology, we assess an AI system for predicting migraine and headache treatment, where data remain local and only model updates are shared. Through co-design sessions with software developers and the assessment team, claims, issues, tensions, resolutions, and monitorable risks were identified and linked via sociotechnical scenarios that reveal improvement paths. Findings were mapped to the European Commission’s High-Level Expert Group on AI requirements and the MIT AI Risk Repository, showing how trustworthiness requirements and risks can be operationalized in line with legislation such as the GDPR and the EU AI Act. The study suggests that cross-jurisdictional governance can support the integration of FML into clinical practice. To our knowledge, this is the first Z-Inspection® review of a federated clinical application mapped end-to-end to the EU AI HLEG requirements. We show that a governance model aligned with technology readiness levels, using coordinated assessment tracks with shared minimum controls and explicit deployment gates, can manage uneven readiness across sites. This work demonstrates cross-border learning over heterogeneous health records by combining structured primary-care data with unstructured neurology notes without transferring raw data. It also provides a practical barrier assessment template for evaluating and improving the trustworthiness of federated clinical AI systems.

This narrative review introduces key concepts in artificial intelligence (AI), data science, and wearable devices aimed at headache clinicians and researchers. PubMed and IEEEXplore were searched to identify relevant studies, and these were reviewed systematically. We identified six primary research topics. First, the most common application of AI and data science is in the diagnosis of headache disorders, with reported accuracies of up to 90%. Second, AI and data science are used for predicting headache disease trajectories and forecasting attacks. Third, prediction of treatment effects and data-driven individualization of treatment prescription demonstrate promising results, with accuracies ranging from 40% to 83%. Fourth, AI and data science can uncover hidden information within headache datasets, offering clinicians deeper insights. Fifth, wearables, combined with AI and data science, can improve remote monitoring and migraine management. Lastly, user experience studies indicate strong interest from both clinicians and patients in adopting these technologies. The potential applications of AI, data science, and wearable device technologies in headache research are vast. However, many studies are small pilot studies, and models often suffer from poor performance, limited reporting, and lack of external validation, which impede generalizability and clinical implementation.

Background: We aimed to develop the first machine learning models to predict citation counts and the translational impact, defined as inclusion in guidelines or policy documents, of headache research, and assess which factors are most predictive. Methods: Bibliometric data and the titles, abstracts, and keywords from 8600 publications in three headache-oriented journals from their inception to 31 December 2017 were used. A series of machine learning models were implemented to predict three classes of 5-year citation count intervals (0–5, 6–14 and, >14 citations); and the translational impact of a publication. Models were evaluated out-of-sample with area under the receiver operating characteristics curve (AUC). Results: The top performing gradient boosting model predicted correct citation count class with an out-of-sample AUC of 0.81. Bibliometric data such as page count, number of references, first and last author citation counts and h-index were among the most important predictors. Prediction of translational impact worked optimally when including both bibliometric data and information from the title, abstract and keywords, reaching an out-of-sample AUC of 0.71 for the top performing random forest model. Conclusion: Citation counts are best predicted by bibliometric data, while models incorporating both bibliometric data and publication content identifies the translational impact of headache research.

Being able to robustly reconstruct 3D faces from 2D images is a topic of pivotal importance for a variety of computer vision branches, such as face analysis and face recognition, whose applications are steadily growing. Unlike 2D facial images, 3D facial data are less affected by lighting conditions and pose. Recent advances in the computer vision field have enabled the use of convolutional neural networks (CNNs) for the production of 3D facial reconstructions from 2D facial images. This paper proposes a novel CNN-based method which targets 3D facial reconstruction from two facial images, one in front and one from the side, as are often available to law enforcement agencies (LEAs). The proposed CNN was trained on both synthetic and real facial data. We show that the proposed network was able to predict 3D faces in the MICC Florence dataset with greater accuracy than the current state-of-the-art. Moreover, a scheme for using the proposed network in cases where only one facial image is available is also presented. This is achieved by introducing an additional network whose task is to generate a rotated version of the original image, which in conjunction with the original facial image, make up the image pair used for reconstruction via the previous method.

It has recently been shown in Re-Identification (Re-ID) work that full-body images of people reveal their somatotype, even after change in apparel. A significant advantage of this biometric trait is that it can easily be captured, even at a distance, as a full-body image of a person, taken by a standard 2D camera. In this work, full-body image-based somatotype is investigated as a novel soft biometric feature for person recognition at a distance and on-the-move. The two common scenarios of (i) identification and (ii) verification are both studied and evaluated. To this end, two different deep networks have been recruited, one for the identification and one for the verification scenario. Experiments have been conducted on popular, publicly available datasets and the results indicate that somatotype can indeed be a valuable biometric trait for identity recognition at a distance and on-the-move (and hence also suitable for non-collaborative individuals) due to the ease of obtaining the required images. This soft biometric trait can be especially useful under a wider biometric fusion scheme.