Enhancing Lung Ultrasound Diagnostics: A Clinical Study on an Artificial Intelligence Tool for the Detection and Quantification of A-Lines and B-Lines
<b>Background/Objective:</b> A-lines and B-lines are key ultrasound markers that differentiate normal from abnormal lung conditions. A-lines are horizontal lines usually seen in normal aerated lungs, while B-lines are linear vertical artifacts associated with lung abnormalities such as p...
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MDPI AG
2024-11-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/14/22/2526 |
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| author | Mahdiar Nekoui Seyed Ehsan Seyed Bolouri Amir Forouzandeh Masood Dehghan Dornoosh Zonoobi Jacob L. Jaremko Brian Buchanan Arun Nagdev Jeevesh Kapur |
| author_facet | Mahdiar Nekoui Seyed Ehsan Seyed Bolouri Amir Forouzandeh Masood Dehghan Dornoosh Zonoobi Jacob L. Jaremko Brian Buchanan Arun Nagdev Jeevesh Kapur |
| author_sort | Mahdiar Nekoui |
| collection | DOAJ |
| description | <b>Background/Objective:</b> A-lines and B-lines are key ultrasound markers that differentiate normal from abnormal lung conditions. A-lines are horizontal lines usually seen in normal aerated lungs, while B-lines are linear vertical artifacts associated with lung abnormalities such as pulmonary edema, infection, and COVID-19, where a higher number of B-lines indicates more severe pathology. This paper aimed to evaluate the effectiveness of a newly released lung ultrasound AI tool (ExoLungAI) in the detection of A-lines and quantification/detection of B-lines to help clinicians in assessing pulmonary conditions. <b>Methods</b>: The algorithm is evaluated on 692 lung ultrasound scans collected from 48 patients (65% males, aged: 55 ± 12.9) following their admission to an Intensive Care Unit (ICU) for COVID-19 symptoms, including respiratory failure, pneumonia, and other complications. <b>Results</b>: ExoLungAI achieved a sensitivity of 91% and specificity of 81% for A-line detection. For B-line detection, it attained a sensitivity of 84% and specificity of 86%. In quantifying B-lines, the algorithm achieved a weighted kappa score of 0.77 (95% CI 0.74 to 0.80) and an ICC of 0.87 (95% CI 0.85 to 0.89), showing substantial agreement between the ground truth and predicted B-line counts. <b>Conclusions</b>: ExoLungAI demonstrates a reliable performance in A-line detection and B-line detection/quantification. This automated tool has greater objectivity, consistency, and efficiency compared to manual methods. Many healthcare professionals including intensivists, radiologists, sonographers, medical trainers, and nurse practitioners can benefit from such a tool, as it assists the diagnostic capabilities of lung ultrasound and delivers rapid responses. |
| format | Article |
| id | doaj-art-052d6a4766164e128a97b9137b6731d8 |
| institution | OA Journals |
| issn | 2075-4418 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj-art-052d6a4766164e128a97b9137b6731d82025-08-20T02:28:04ZengMDPI AGDiagnostics2075-44182024-11-011422252610.3390/diagnostics14222526Enhancing Lung Ultrasound Diagnostics: A Clinical Study on an Artificial Intelligence Tool for the Detection and Quantification of A-Lines and B-LinesMahdiar Nekoui0Seyed Ehsan Seyed Bolouri1Amir Forouzandeh2Masood Dehghan3Dornoosh Zonoobi4Jacob L. Jaremko5Brian Buchanan6Arun Nagdev7Jeevesh Kapur8Exo Imaging, Santa Clara, CA 95054, USAExo Imaging, Santa Clara, CA 95054, USAExo Imaging, Santa Clara, CA 95054, USAExo Imaging, Santa Clara, CA 95054, USAExo Imaging, Santa Clara, CA 95054, USADepartment of Radiology & Diagnostic Imaging, University of Alberta, Edmonton, AB T6G 2R3, CanadaDepartment of Critical Care Medicine, University of Alberta, Edmonton, AB T6G 2R3, CanadaAlameda Health System, Highland General Hospital, University of California San Francisco, San Francisco, CA 94143, USADepartment of Diagnostic Imaging, National University of Singapore, Singapore 119228, Singapore<b>Background/Objective:</b> A-lines and B-lines are key ultrasound markers that differentiate normal from abnormal lung conditions. A-lines are horizontal lines usually seen in normal aerated lungs, while B-lines are linear vertical artifacts associated with lung abnormalities such as pulmonary edema, infection, and COVID-19, where a higher number of B-lines indicates more severe pathology. This paper aimed to evaluate the effectiveness of a newly released lung ultrasound AI tool (ExoLungAI) in the detection of A-lines and quantification/detection of B-lines to help clinicians in assessing pulmonary conditions. <b>Methods</b>: The algorithm is evaluated on 692 lung ultrasound scans collected from 48 patients (65% males, aged: 55 ± 12.9) following their admission to an Intensive Care Unit (ICU) for COVID-19 symptoms, including respiratory failure, pneumonia, and other complications. <b>Results</b>: ExoLungAI achieved a sensitivity of 91% and specificity of 81% for A-line detection. For B-line detection, it attained a sensitivity of 84% and specificity of 86%. In quantifying B-lines, the algorithm achieved a weighted kappa score of 0.77 (95% CI 0.74 to 0.80) and an ICC of 0.87 (95% CI 0.85 to 0.89), showing substantial agreement between the ground truth and predicted B-line counts. <b>Conclusions</b>: ExoLungAI demonstrates a reliable performance in A-line detection and B-line detection/quantification. This automated tool has greater objectivity, consistency, and efficiency compared to manual methods. Many healthcare professionals including intensivists, radiologists, sonographers, medical trainers, and nurse practitioners can benefit from such a tool, as it assists the diagnostic capabilities of lung ultrasound and delivers rapid responses.https://www.mdpi.com/2075-4418/14/22/2526A-linesB-lineslung ultrasoundmachine learningartificial intelligence |
| spellingShingle | Mahdiar Nekoui Seyed Ehsan Seyed Bolouri Amir Forouzandeh Masood Dehghan Dornoosh Zonoobi Jacob L. Jaremko Brian Buchanan Arun Nagdev Jeevesh Kapur Enhancing Lung Ultrasound Diagnostics: A Clinical Study on an Artificial Intelligence Tool for the Detection and Quantification of A-Lines and B-Lines Diagnostics A-lines B-lines lung ultrasound machine learning artificial intelligence |
| title | Enhancing Lung Ultrasound Diagnostics: A Clinical Study on an Artificial Intelligence Tool for the Detection and Quantification of A-Lines and B-Lines |
| title_full | Enhancing Lung Ultrasound Diagnostics: A Clinical Study on an Artificial Intelligence Tool for the Detection and Quantification of A-Lines and B-Lines |
| title_fullStr | Enhancing Lung Ultrasound Diagnostics: A Clinical Study on an Artificial Intelligence Tool for the Detection and Quantification of A-Lines and B-Lines |
| title_full_unstemmed | Enhancing Lung Ultrasound Diagnostics: A Clinical Study on an Artificial Intelligence Tool for the Detection and Quantification of A-Lines and B-Lines |
| title_short | Enhancing Lung Ultrasound Diagnostics: A Clinical Study on an Artificial Intelligence Tool for the Detection and Quantification of A-Lines and B-Lines |
| title_sort | enhancing lung ultrasound diagnostics a clinical study on an artificial intelligence tool for the detection and quantification of a lines and b lines |
| topic | A-lines B-lines lung ultrasound machine learning artificial intelligence |
| url | https://www.mdpi.com/2075-4418/14/22/2526 |
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