Diagnostic AI and Cardiac Diseases

Ozsahin, Dilber Uzun; Ozgocmen, Cemre; Balcioglu, Ozlem; Ozsahin, Ilker; Uzun, Berna

doi:10.3390/diagnostics12122901

Cited by 7 publications

(3 citation statements)

References 45 publications

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“…One potential area of future development is engineering in medical AI and medicine; there will probably be discussion in the future about physicians with exhaustive knowledge of medical AI. For significant technological progress and innovation, close collaborations between healthcare engineering systems and physicians are needed [ 250 ].…”

Section: Discussionmentioning

confidence: 99%

“…What can be done about patients’ data privacy and who should be trained to protect it? How can we prevent doctors’ judgmental standards from falling due to reliance on AI for diagnosis, which may become a serious problem in a few generations [ 250 ]? For correct and complete implementation, this side of AI must be addressed, and for the moment, it is one of the most sensitive issues.…”

Section: Discussionmentioning

confidence: 99%

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Revolutionizing Cardiology through Artificial Intelligence—Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment—A Comprehensive Review of the Past 5 Years

Stamate,

Piraianu,

Ciobotaru

et al. 2024

Diagnostics

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Background: Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology. Results: We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology. Conclusions: The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Revolutionizing Cardiology through Artificial Intelligence—Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment—A Comprehensive Review of the Past 5 Years

Stamate,

Piraianu,

Ciobotaru

et al. 2024

Diagnostics

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“…Recently, Arti cial Intelligence (AI) and deep learning technologies have revolutionized image analysis and predictive modelling in healthcare 4 . Recent study has indicated that chest radiography can provide information not only traditional diagnosis tool but on cardiac function and biomarker of ageing [5][6][7][8] . In our previous study, physical characteristics were extracted from chest radiography to develop height and weight predictive model that assessed the nutritional status of older bedridden adults 9 .…”

Section: Introductionmentioning

confidence: 99%

Advancing Liver Disease Sarcopenia Prediction: A Chest- Radiograph–Based Model for Older Adults

Sasaki,

Nakao,

Mawatari

et al. 2024

Preprint

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Background Sarcopenia has been identified as a significant factor affecting the quality of life and prognosis of ageing population. We have been developing a deep learning model to predict height, weight for appropriate nutritional intervention in bedridden patients based on chest radiographs examinations. In this study, we developed and validated a model for predicting sarcopenia in patients with liver disease based on chest radiographs. The model was developed and validated in patients with liver disease based on chest radiographs. Methods A total of approximately 10842 male and chest Radiograph examinations, including physical examinations conducted in the past 15 years, were subjected to the study. BMI data were used as the teacher data and validation data, and a discriminant model S-CNN was developed using cut off BMI values (18.5, 19, 20). A convolutional neural network ResNet-152 was used to develop the model, and the model was trained on an Nvidia RTX A6000 using Python 3.8 and Pytorch 1.8.1. To validate sarcopenia, we used x-rays of 22 liver disease patients (13 sarcopenia cases) over the age of 65 years, in which grip strength and limb skeletal muscle mass were measured. Limb skeletal muscle mass was measured by bioelectrical impedance (BIA) and skeletal muscle mass index (SMI) was obtained. 2were determined as sarcopenia. Sensitivity, specificity, accuracy, and F1 score were used to evaluate the performance of the deep learning model; Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) were used to evaluate the prediction accuracy of sarcopenia patients with liver disease. Results The S-CNN model showed a sensitivity, specificity, accuracy, and F 1 scores were 98.0%, 98.0%, 97.0%, and 98%, respectively. Next, the results were validated with chest radiographs including liver disease sarcopenia, AUC = 0.62 (BMI cut off = 18.5), AUC = 0.62 (BMI cut off = 19), and AUC = 0.77 (BMI cut off = 20). Conclusion The highest AUC for prediction of sarcopenia in patients with liver disease by chest radiography was found at BMI (cut off = 20). Development of a more accurate model and its validation on a large scale are expected to realize a simplified pickup of sarcopenia in elderly patients with liver disease.

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Improving patient’s medical history classification using a feature construction approach based on situation awareness and granular computing

Lepore,

Plenzich,

Tufano

et al. 2024

Neural Comput & Applic

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Healthcare decision support systems aid physicians in disease classification by analyzing patients’ medical histories to suggest preliminary diagnoses. As physicians largely base their analysis on anamnesis, integrating this process into an automated recommendation system can expedite decision-making and transition to relevant clinical investigations, thus enhancing efficiency in diagnosing potential pathologies. In this research, an innovative method for feature construction is introduced, drawing on the concepts of Situation Awareness and Granular Computing. The aim of this method is to enhance the performance of out-of-the-box classification algorithms used in machine learning. The approach is specifically tailored to mimic physicians’ cognitive processes when analyzing a patient’s medical history, resulting in the generation of new, information-dense features that can be used for classification tasks. By employing this strategy, a deeper comprehension of the data can be achieved, as well as a more precise categorization of anamneses in relation to possible medical conditions. To authenticate the efficacy of the proposed technique, three major disease categories, namely cardiac, gastrointestinal, and thyroid, were considered. The dataset comprised 1213 medical histories. The experimental results indicate that the study’s six classifiers attained a balanced accuracy exceeding 90%. Among these, the SVM classifier demonstrated the highest balanced accuracy at 93%. Overall, the proposed approach resulted in an average increase of 16 percentage points in balanced accuracy, representing an improvement over the traditional methods commonly employed in machine learning. This approach could be integrated into a clinical decision support system, aiding physicians in accurately identifying necessary investigations and expediting diagnosis.

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Diagnostic AI and Cardiac Diseases

Cited by 7 publications

References 45 publications

Revolutionizing Cardiology through Artificial Intelligence—Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment—A Comprehensive Review of the Past 5 Years

Revolutionizing Cardiology through Artificial Intelligence—Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment—A Comprehensive Review of the Past 5 Years

Advancing Liver Disease Sarcopenia Prediction: A Chest- Radiograph–Based Model for Older Adults

Improving patient’s medical history classification using a feature construction approach based on situation awareness and granular computing

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