Artificial Intelligence and Quantum Technology in Multidisciplinary Data Analysis: Applications in Medicine

Abstract

Background: Eclampsia is a severe obstetric complication with high maternal morbidity and mortality, often associated with neurological manifestations. Neuroimaging plays a vital role in assessing disease severity, while Artificial Intelligence (AI) and emerging quantum technologies hold promise for enhancing prognostic accuracy in clinical outcomes.

Aim: To evaluate the prognostic value of neuroimaging in eclampsia using AI algorithms for predicting clinical outcomes.

Objectives: 1. To develop AI models analyzing CT findings for predicting ICU admission, need for mechanical ventilation, seizure recurrence, and hospital mortality.

1. To compare outcomes between patients with abnormal and normal neuroimaging findings. 3. To assess the utility of AI-augmented neuroimaging in guiding personalized management of eclampsia.

Methods: A retrospective observational study was conducted at a tertiary care teaching hospital over two years (January 2023–December 2024). Data from 200 eclampsia patients undergoing CT brain imaging were analyzed. Preprocessed imaging and clinical data were used to train machine-learning models (CNNs and ensemble methods). Model performance was assessed using accuracy, sensitivity, specificity, AUC-ROC, and decision curve analysis. Comparative analysis was performed between the abnormal and normal CT groups.

Results: Of 200 patients, 117 (58.5%) had abnormal CT findings, predominantly vasogenic edema/PRES (25.5%), intracranial hemorrhage (14.5%), and acute infarct (6.5%). Abnormal CT was significantly associated with worse outcomes: ICU admission (OR 4.10, p<0.001), mechanical ventilation (OR 3.07, p=0.002), seizure recurrence (OR 2.53, p=0.013), and mortality (OR 3.36, p=0.035). AI models demonstrated strong predictive performance with AUC values of 0.85–0.90 across outcomes, outperforming baseline models and providing improved discrimination, calibration, and net benefit.

Conclusion: AI-augmented neuroimaging significantly improves prognostic accuracy in eclampsia compared with conventional analysis. Integration of AI with neuroimaging enhances early risk stratification and supports personalized management, while quantum frameworks represent a future pathway for handling high-dimensional datasets in clinical medicine.