Key result
Multimodal deep learning model classifies ASA physical status with ~97% accuracy.
Why the study?
ASA Physical Status Classification scoring varies among clinicians due to subjective assessments, making standardization difficult for preoperative risk evaluation.
Does a deep learning model integrating lung images, ECG, and clinical data accurately predict the ASA Physical Status Classification in preoperative patients?
Does a deep learning model integrating lung images, ECG, and clinical data accurately predict the ASA Physical Status Classification in preoperative patients?
A deep learning model combining lung images, ECG, and clinical data can predict the ASA Physical Status Classification with 97.3% accuracy, offering a more objective tool for preoperative risk management.
Promising multimodal accuracy for objective ASA-PS; leaves open prospective validation before clinical adoption.
Background/Objectives: Objective assessment of patients’ physical health status before surgical interventions is crucial in predicting surgical risks and planning patient management. The American Society of Anesthesiologists Physical Status Classification (ASA-PS) is one of the most widely used scales in this context. It plays a vital role in clinical decision-making processes. However, ASA scoring can vary among clinicians due to its reliance on subjective assessments, making it difficult to standardize. Methods: In this study, a deep learning-based model was developed to classify ASA from patient data automatically. Three different data types were used in this study: lung images, Electrocardiogram (ECG) data, and clinical data. For the proposed ASA prediction model, features were extracted from each data type. Features were extracted from ECG and Computed Tomography (CT) images using five different Convolutional Neural Network (CNN) architectures, while statistical features were derived from the clinical data. The features obtained from the CNN architecture with the highest classification performance were combined with the statistical features. These combined features were then processed using seven different classifiers. Results: The developed model was evaluated with performance metrics such as accuracy, precision, F1 score, and Receiver Operating Characteristic (ROC) curve. As a result, a high accuracy value of 97.30% was obtained. Conclusions: This study aims to make preoperative risk management for ASA classification more objective and consistent, and the results obtained support this aim.
No takes yet. Share an insight, caveat, or question.
Yıldırım et al. (2026) studied Preoperative surgical risk (ASA Physical Status). Deep learning-based ASA prediction model integrating lung images, ECG, and clinical data was evaluated on Classification accuracy of ASA score. A deep learning-based model integrating lung images, ECG data, and clinical features automatically classified American Society of Anesthesiologists physical status with 97.30% accuracy.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: