Validation Study of ASA Physical Status Prediction Model Developed Through Holistic Integration of Lung Images, Electrocardiogram Data and Clinical Data
4 affiliations
- 1Department of Urology, Elazig Fethi Sekin City Hospital, University of Health Sciences, 23280 Elazig, Türkiye
- 2Clinics of Anesthesiology and Reanimation, Elazig Fethi Sekin City Hospital, 23280 Elazig, Türkiye
- 3Department of Software Engineering, Malatya Turgut Ozal University, 44210 Malatya, Türkiye
- 4Department of Artificial Intelligence and Data Engineering, Firat University, 23119 Elazig, Türkiye
Abstract
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.
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BibTeX
@article{yildirim2026validation,
title = {{Validation Study of ASA Physical Status Prediction Model Developed Through Holistic Integration of Lung Images, Electrocardiogram Data and Clinical Data}},
author = {Kadir Yildirim and Serpil Bayindir and Mucahit Karaduman and Muhammed Yildirim},
journal = {Journal of Clinical Medicine},
year = {2026},
month = oct,
doi = {10.3390/jcm15207965},
url = {https://www.mdpi.com/2077-0383/15/20/7965},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Validation Study of ASA Physical Status Prediction Model Developed Through Holistic Integration of Lung Images, Electrocardiogram Data and Clinical Data
AU - Kadir Yildirim
AU - Serpil Bayindir
AU - Mucahit Karaduman
AU - Muhammed Yildirim
PY - 2026
DA - 2026/10/01
JO - Journal of Clinical Medicine
DO - 10.3390/jcm15207965
UR - https://www.mdpi.com/2077-0383/15/20/7965
AB - 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.
SN - 2077-0383
ER -