Empowering Medical Students: Harnessing Artificial Intelligence for Precision Point-of-Care Echocardiography Assessment of Left Ventricular Ejection Fraction
Introduction. Point-of-care ultrasound (POCUS) use is now universal among nonexperts. Artificial intelligence (AI) is currently employed by nonexperts in various imaging modalities to assist in diagnosis and decision making. Aim. To evaluate the diagnostic accuracy of POCUS, operated by medical stud...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Hindawi-Wiley
2023-01-01
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| Series: | International Journal of Clinical Practice |
| Online Access: | http://dx.doi.org/10.1155/2023/5225872 |
| _version_ | 1797401955784458240 |
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| author | Ziv Dadon Amir Orlev Adi Butnaru David Rosenmann Michael Glikson Shmuel Gottlieb Evan Avraham Alpert |
| author_facet | Ziv Dadon Amir Orlev Adi Butnaru David Rosenmann Michael Glikson Shmuel Gottlieb Evan Avraham Alpert |
| author_sort | Ziv Dadon |
| collection | DOAJ |
| description | Introduction. Point-of-care ultrasound (POCUS) use is now universal among nonexperts. Artificial intelligence (AI) is currently employed by nonexperts in various imaging modalities to assist in diagnosis and decision making. Aim. To evaluate the diagnostic accuracy of POCUS, operated by medical students with the assistance of an AI-based tool for assessing the left ventricular ejection fraction (LVEF) of patients admitted to a cardiology department. Methods. Eight students underwent a 6-hour didactic and hands-on training session. Participants used a hand-held ultrasound device (HUD) equipped with an AI-based tool for the automatic evaluation of LVEF. The clips were assessed for LVEF by three methods: visually by the students, by students + the AI-based tool, and by the cardiologists. All LVEF measurements were compared to formal echocardiography completed within 24 hours and were evaluated for LVEF using the Simpson method and eyeballing assessment by expert echocardiographers. Results. The study included 88 patients (aged 58.3 ± 16.3 years). The AI-based tool measurement was unsuccessful in 6 cases. Comparing LVEF reported by students’ visual evaluation and students + AI vs. cardiologists revealed a correlation of 0.51 and 0.83, respectively. Comparing these three evaluation methods with the echocardiographers revealed a moderate/substantial agreement for the students + AI and cardiologists but only a fair agreement for the students’ visual evaluation. Conclusion. Medical students’ utilization of an AI-based tool with a HUD for LVEF assessment achieved a level of accuracy similar to that of cardiologists. Furthermore, the use of AI by the students achieved moderate to substantial inter-rater reliability with expert echocardiographers’ evaluation. |
| first_indexed | 2024-03-09T02:18:14Z |
| format | Article |
| id | doaj.art-b6a3f29fab2349ecb10776c9c58c1a9d |
| institution | Directory Open Access Journal |
| issn | 1742-1241 |
| language | English |
| last_indexed | 2024-03-09T02:18:14Z |
| publishDate | 2023-01-01 |
| publisher | Hindawi-Wiley |
| record_format | Article |
| series | International Journal of Clinical Practice |
| spelling | doaj.art-b6a3f29fab2349ecb10776c9c58c1a9d2023-12-07T00:00:22ZengHindawi-WileyInternational Journal of Clinical Practice1742-12412023-01-01202310.1155/2023/5225872Empowering Medical Students: Harnessing Artificial Intelligence for Precision Point-of-Care Echocardiography Assessment of Left Ventricular Ejection FractionZiv Dadon0Amir Orlev1Adi Butnaru2David Rosenmann3Michael Glikson4Shmuel Gottlieb5Evan Avraham Alpert6Jesselson Integrated Heart CenterJesselson Integrated Heart CenterJesselson Integrated Heart CenterJesselson Integrated Heart CenterJesselson Integrated Heart CenterJesselson Integrated Heart CenterFaculty of MedicineIntroduction. Point-of-care ultrasound (POCUS) use is now universal among nonexperts. Artificial intelligence (AI) is currently employed by nonexperts in various imaging modalities to assist in diagnosis and decision making. Aim. To evaluate the diagnostic accuracy of POCUS, operated by medical students with the assistance of an AI-based tool for assessing the left ventricular ejection fraction (LVEF) of patients admitted to a cardiology department. Methods. Eight students underwent a 6-hour didactic and hands-on training session. Participants used a hand-held ultrasound device (HUD) equipped with an AI-based tool for the automatic evaluation of LVEF. The clips were assessed for LVEF by three methods: visually by the students, by students + the AI-based tool, and by the cardiologists. All LVEF measurements were compared to formal echocardiography completed within 24 hours and were evaluated for LVEF using the Simpson method and eyeballing assessment by expert echocardiographers. Results. The study included 88 patients (aged 58.3 ± 16.3 years). The AI-based tool measurement was unsuccessful in 6 cases. Comparing LVEF reported by students’ visual evaluation and students + AI vs. cardiologists revealed a correlation of 0.51 and 0.83, respectively. Comparing these three evaluation methods with the echocardiographers revealed a moderate/substantial agreement for the students + AI and cardiologists but only a fair agreement for the students’ visual evaluation. Conclusion. Medical students’ utilization of an AI-based tool with a HUD for LVEF assessment achieved a level of accuracy similar to that of cardiologists. Furthermore, the use of AI by the students achieved moderate to substantial inter-rater reliability with expert echocardiographers’ evaluation.http://dx.doi.org/10.1155/2023/5225872 |
| spellingShingle | Ziv Dadon Amir Orlev Adi Butnaru David Rosenmann Michael Glikson Shmuel Gottlieb Evan Avraham Alpert Empowering Medical Students: Harnessing Artificial Intelligence for Precision Point-of-Care Echocardiography Assessment of Left Ventricular Ejection Fraction International Journal of Clinical Practice |
| title | Empowering Medical Students: Harnessing Artificial Intelligence for Precision Point-of-Care Echocardiography Assessment of Left Ventricular Ejection Fraction |
| title_full | Empowering Medical Students: Harnessing Artificial Intelligence for Precision Point-of-Care Echocardiography Assessment of Left Ventricular Ejection Fraction |
| title_fullStr | Empowering Medical Students: Harnessing Artificial Intelligence for Precision Point-of-Care Echocardiography Assessment of Left Ventricular Ejection Fraction |
| title_full_unstemmed | Empowering Medical Students: Harnessing Artificial Intelligence for Precision Point-of-Care Echocardiography Assessment of Left Ventricular Ejection Fraction |
| title_short | Empowering Medical Students: Harnessing Artificial Intelligence for Precision Point-of-Care Echocardiography Assessment of Left Ventricular Ejection Fraction |
| title_sort | empowering medical students harnessing artificial intelligence for precision point of care echocardiography assessment of left ventricular ejection fraction |
| url | http://dx.doi.org/10.1155/2023/5225872 |
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