Impact of Automated Prognostication on Traumatic Brain Injury Care: A Focus Group Study.

BACKGROUND: Prognosticating outcomes for traumatic brain injury (TBI) patients is challenging due to the required specialized skills and variability among clinicians. Recent attempts to standardize TBI prognosis have leveraged machine learning (ML) methodologies. This study evaluates the necessity and influence of ML-assisted TBI prognostication through healthcare professionals' perspectives via focus group discussions. METHODS: Two virtual focus groups included ten key TBI care stakeholders (one neurosurgeon, two emergency clinicians, one internist, two radiologists, one registered nurse, two researchers in ML and healthcare and one patient representative). They answered six open-ended questions about their perceptions and potential ML use in TBI prognostication. Transcribed focus group discussions were thematically analyzed using qualitative data analysis software. RESULTS: The study captured diverse perceptions and interests in TBI prognostication across clinical specialties. Notably, certain clinicians who currently do not prognosticate expressed an interest in doing so independently provided they had access to ML support. Concerns included ML's accuracy and the need for proficient ML researchers in clinical settings. The consensus suggested using ML as a secondary consultation tool and promoting collaboration with internal or external research resources. Participants believed ML prognostication could enhance disposition planning and standardize care regardless of clinician expertise or injury severity. There was no evidence of perceived bias or interference during the discussions. CONCLUSION: Our findings revealed an overall positive attitude toward ML-based prognostication. Despite raising multiple concerns, the focus group discussions were particularly valuable in underscoring the potential of ML in democratizing and standardizing TBI prognosis practices.

Using diffusion models to generate synthetic labeled data for medical image segmentation.

PURPOSE: Medical image analysis has become a prominent area where machine learning has been applied. However, high-quality, publicly available data are limited either due to patient privacy laws or the time and cost required for experts to annotate images. In this retrospective study, we designed and evaluated a pipeline to generate synthetic labeled polyp images for augmenting medical image segmentation models with the aim of reducing this data scarcity. METHODS: We trained diffusion models on the HyperKvasir dataset, comprising 1000 images of polyps in the human GI tract from 2008 to 2016. Qualitative expert review, Fréchet Inception Distance (FID), and Multi-Scale Structural Similarity (MS-SSIM) were tested for evaluation. Additionally, various segmentation models were trained with the generated data and evaluated using Dice score (DS) and Intersection over Union (IoU). RESULTS: Our pipeline produced images more akin to real polyp images based on FID scores. Segmentation model performance also showed improvements over GAN methods when trained entirely, or partially, with synthetic data, despite requiring less compute for training. Moreover, the improvement persists when tested on different datasets, showcasing the transferability of the generated images. CONCLUSIONS: The proposed pipeline produced realistic image and mask pairs which could reduce the need for manual data annotation when performing a machine learning task. We support this use case by showing that the methods proposed in this study enhanced segmentation model performance, as measured by Dice and IoU scores, when trained fully or partially on synthetic data.

Increased Prevalence of the Electrocardiographic Early Repolarization Pattern in Young Patients With Vagally Mediated Syncope: A Case-Control Study.

Background: Electrocardiographic early repolarization (EER) is linked with idiopathic ventricular fibrillation in adults. It is frequently seen in children, with poorly understood significance. Some evidence suggests that it could be a vagally mediated phenomenon. A retrospective case-control study was undertaken to test the hypothesis that EER is more common among children with typical vasovagal syncope (VVS) than among their peers with nonvagal syncope (NVS) or with no syncope. Methods: Patients aged 4-18 years with syncope were identified by a single-centre database search followed by a review of history for features of VVS (n = 150) or NVS (n = 84). The first available electrocardiogram (ECG) for VVS or for NVS was retrieved. Age- and sex-matched children with no known syncope or heart disease were then identified (n = 216). ECGs were assessed separately for EER based on published criteria by 2 observers blinded to patients' clinical status. Results: Mean age was 12.3 ± 3.2 years, and heart rate was 74.2 ± 16.5 beats/min. EER was more prevalent in VVS (33.3%) than among patients with NVS (19.1%; odds ratio: 2.29; confidence interval: 1.32-5.50) or among those with no syncope (12.5%; odds ratio: 3.14; confidence interval: 1.81-5.46). Heart rates were significantly lower in VVS and NVS (heart rate: 70.1 ± 13.8 and 70.7 ± 12.4 beats/min, respectively) compared with children with no syncope (heart rate: 78.2 ± 18.0 beats/min), both P < 0.001. Conclusions: EER is more common in paediatric patients with VVS than those with NVS or without syncope, consistent with a possible vagal contribution to the ECG finding.

Contexte: La repolarisation précoce (RP) à l'électrocardiogramme (ECG) est liée à une fibrillation ventriculaire idiopathique chez les adultes. Fréquente chez les enfants, sa signification est toutefois nébuleuse. Certaines données laissent penser qu'il pourrait s'agir d'un phénomène d'origine vagale. Une étude rétrospective cas-témoins a donc été réalisée dans le but de vérifier l'hypothèse selon laquelle la RP à l'ECG est plus courante chez les enfants atteints de syncope vasovagale (SVV) typique que chez leurs pairs atteints de syncope non vagale (SNV) ou non atteints de syncope. Méthodologie: Des patients de 4 à 18 ans atteints de syncope ont été recensés au moyen d'une recherche dans la base de données d'un centre, suivie d'un examen des antécédents visant à retracer des manifestations de SVV (n = 150) ou de SNV (n = 84). Le premier ECG disponible traduisant une SVV ou une SNV a été récupéré. Un appariement selon l'âge et le sexe entre les sujets atteints et des enfants qui n'étaient pas atteints de syncope ni de maladie cardiaque (n = 216) a ensuite été effectué. Deux observateurs qui ne connaissaient pas l'état clinique des enfants ont évalué les ECG séparément, à la recherche d'une RP, en se basant sur les critères publiés. Résultats: L'âge moyen des sujets était de 12,3 ± 3,2 ans et la fréquence cardiaque moyenne, de 74,2 ± 16,5 battements/minute. La prévalence de la RP à l'ECG était plus élevée chez les patients atteints de SVV (33,3 %) que chez les patients atteints de SNV (19,1 %; rapport de cotes [RC] : 2,29; intervalle de confiance [IC] : 1,32-5,50) ou les enfants non atteints de syncope (12,5 %; RC : 3,14; IC : 1,81-5,46). La fréquence cardiaque (FC) était significativement plus faible chez les sujets atteints de SVV ou de SNV (FC : 70,1 ± 13,8 et 70,7 ± 12,4 battements/minute, respectivement), en comparaison des enfants non atteints de syncope (FC : 78,2 ± 18,0 battements/minute); p < 0,001 dans les deux cas. Conclusion: La repolarisation précoce à l'ECG est plus courante chez les enfants atteints de syncope vasovagale que chez les enfants atteints de syncope non vagale ou non atteints de syncope, ce qui concorde avec une possible composante vagale dans le tracé de l'ECG.

Causal Forest Machine Learning Analysis of Parkinson's Disease in Resting-State Functional Magnetic Resonance Imaging.

In recent years, Artificial Intelligence has been used to assist healthcare professionals in detecting and diagnosing neurodegenerative diseases. In this study, we propose a methodology to analyze functional Magnetic Resonance Imaging signals and perform classification between Parkinson's disease patients and healthy participants using Machine Learning algorithms. In addition, the proposed approach provides insights into the brain regions affected by the disease. The functional Magnetic Resonance Imaging from the PPMI and 1000-FCP datasets were pre-processed to extract time series from 200 brain regions per participant, resulting in 11,600 features. Causal Forest and Wrapper Feature Subset Selection algorithms were used for dimensionality reduction, resulting in a subset of features based on their heterogeneity and association with the disease. We utilized Logistic Regression and XGBoost algorithms to perform PD detection, achieving 97.6% accuracy, 97.5% F1 score, 97.9% precision, and 97.7%recall by analyzing sets with fewer than 300 features in a population including men and women. Finally, Multiple Correspondence Analysis was employed to visualize the relationships between brain regions and each group (women with Parkinson, female controls, men with Parkinson, male controls). Associations between the Unified Parkinson's Disease Rating Scale questionnaire results and affected brain regions in different groups were also obtained to show another use case of the methodology. This work proposes a methodology to (1) classify patients and controls with Machine Learning and Causal Forest algorithm and (2) visualize associations between brain regions and groups, providing high-accuracy classification and enhanced interpretability of the correlation between specific brain regions and the disease across different groups.

Development of a Multimodal Machine Learning-Based Prognostication Model for Traumatic Brain Injury Using Clinical Data and Computed Tomography Scans: A CENTER-TBI and CINTER-TBI Study.

Computed tomography (CT) is an important imaging modality for guiding prognostication in patients with traumatic brain injury (TBI). However, because of the specialized expertise necessary, timely and dependable TBI prognostication based on CT imaging remains challenging. This study aimed to enhance the efficiency and reliability of TBI prognostication by employing machine learning (ML) techniques on CT images. A retrospective analysis was conducted on the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) data set (n = 1016). An ML-driven binary classifier was developed to predict favorable or unfavorable outcomes at 6 months post-injury. The prognostic performance was assessed using the area under the curve (AUC) over fivefold cross-validation and compared with conventional models that depend on clinical variables and CT scoring systems. An external validation was performed using the Comparative Indian Neurotrauma Effectiveness Research in Traumatic Brain Injury (CINTER-TBI) data set (n = 348). The developed model achieved superior performance without the necessity for manual CT assessments (AUC = 0.846 [95% CI: 0.843-0.849]) compared with the model based on the clinical and laboratory variables (AUC = 0.817 [95% CI: 0.814-0.820]) and established CT scoring systems requiring manual interpretations (AUC = 0.829 [95% CI: 0.826-0.832] for Marshall and 0.838 [95% CI: 0.835-0.841] for International Mission for Prognosis and Analysis of Clinical Trials in TBI [IMPACT]). The external validation demonstrated the prognostic capacity of the developed model to be significantly better (AUC = 0.859 [95% CI: 0.857-0.862]) than the model using clinical variables (AUC = 0.809 [95% CI: 0.798-0.820]). This study established an ML-based model that provides efficient and reliable TBI prognosis based on CT scans, with potential implications for earlier intervention and improved patient outcomes.