Suboptimal Chest Radiography and Artificial Intelligence: The Problem and the Solution
Chest radiographs (CXR) are the most performed imaging tests and rank high among the radiographic exams with suboptimal quality and high rejection rates. Suboptimal CXRs can cause delays in patient care and pitfalls in radiographic interpretation, given their ubiquitous use in the diagnosis and mana...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/3/412 |
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| author | Giridhar Dasegowda Mannudeep K. Kalra Alain S. Abi-Ghanem Chiara D. Arru Monica Bernardo Luca Saba Doris Segota Zhale Tabrizi Sanjaya Viswamitra Parisa Kaviani Lina Karout Keith J. Dreyer |
| author_facet | Giridhar Dasegowda Mannudeep K. Kalra Alain S. Abi-Ghanem Chiara D. Arru Monica Bernardo Luca Saba Doris Segota Zhale Tabrizi Sanjaya Viswamitra Parisa Kaviani Lina Karout Keith J. Dreyer |
| author_sort | Giridhar Dasegowda |
| collection | DOAJ |
| description | Chest radiographs (CXR) are the most performed imaging tests and rank high among the radiographic exams with suboptimal quality and high rejection rates. Suboptimal CXRs can cause delays in patient care and pitfalls in radiographic interpretation, given their ubiquitous use in the diagnosis and management of acute and chronic ailments. Suboptimal CXRs can also compound and lead to high inter-radiologist variations in CXR interpretation. While advances in radiography with transitions to computerized and digital radiography have reduced the prevalence of suboptimal exams, the problem persists. Advances in machine learning and artificial intelligence (AI), particularly in the radiographic acquisition, triage, and interpretation of CXRs, could offer a plausible solution for suboptimal CXRs. We review the literature on suboptimal CXRs and the potential use of AI to help reduce the prevalence of suboptimal CXRs. |
| first_indexed | 2024-03-11T09:48:55Z |
| format | Article |
| id | doaj.art-3d72d61288c74179a089e74f35413848 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T09:48:55Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-3d72d61288c74179a089e74f354138482023-11-16T16:24:20ZengMDPI AGDiagnostics2075-44182023-01-0113341210.3390/diagnostics13030412Suboptimal Chest Radiography and Artificial Intelligence: The Problem and the SolutionGiridhar Dasegowda0Mannudeep K. Kalra1Alain S. Abi-Ghanem2Chiara D. Arru3Monica Bernardo4Luca Saba5Doris Segota6Zhale Tabrizi7Sanjaya Viswamitra8Parisa Kaviani9Lina Karout10Keith J. Dreyer11Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USADepartment of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USADepartment of Diagnostic Radiology, American University of Beirut Medical Center, Beirut 11-0236, LebanonDepartment of Radiology, Azienda Ospedaliera G. Brotzu, 09134 Cagliari, ItalyDepartment of Radiology, Hospital Miguel Soeiro—UNIMED, Sorocaba 18052-210, BrazilDepartment of Radiology, Azienda Ospedaliera Universitaria di Cagliari, 09123 Cagliari, ItalyMedical Physics and Radiation Protection Department, Clinical Hospital Centre Rijeka, 51000 Rijeka, CroatiaRadiology Department, Iran University of Medical Sciences, Tehran 14535, IranDepartment of Radiodiagnosis, Sri Sathya Sai Institute of Higher Medical Sciences, Whitefield 560066, IndiaDepartment of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USADepartment of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USADepartment of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USAChest radiographs (CXR) are the most performed imaging tests and rank high among the radiographic exams with suboptimal quality and high rejection rates. Suboptimal CXRs can cause delays in patient care and pitfalls in radiographic interpretation, given their ubiquitous use in the diagnosis and management of acute and chronic ailments. Suboptimal CXRs can also compound and lead to high inter-radiologist variations in CXR interpretation. While advances in radiography with transitions to computerized and digital radiography have reduced the prevalence of suboptimal exams, the problem persists. Advances in machine learning and artificial intelligence (AI), particularly in the radiographic acquisition, triage, and interpretation of CXRs, could offer a plausible solution for suboptimal CXRs. We review the literature on suboptimal CXRs and the potential use of AI to help reduce the prevalence of suboptimal CXRs.https://www.mdpi.com/2075-4418/13/3/412artificial intelligencechest X-raycomputer-assisted image processingquality improvementradiography |
| spellingShingle | Giridhar Dasegowda Mannudeep K. Kalra Alain S. Abi-Ghanem Chiara D. Arru Monica Bernardo Luca Saba Doris Segota Zhale Tabrizi Sanjaya Viswamitra Parisa Kaviani Lina Karout Keith J. Dreyer Suboptimal Chest Radiography and Artificial Intelligence: The Problem and the Solution Diagnostics artificial intelligence chest X-ray computer-assisted image processing quality improvement radiography |
| title | Suboptimal Chest Radiography and Artificial Intelligence: The Problem and the Solution |
| title_full | Suboptimal Chest Radiography and Artificial Intelligence: The Problem and the Solution |
| title_fullStr | Suboptimal Chest Radiography and Artificial Intelligence: The Problem and the Solution |
| title_full_unstemmed | Suboptimal Chest Radiography and Artificial Intelligence: The Problem and the Solution |
| title_short | Suboptimal Chest Radiography and Artificial Intelligence: The Problem and the Solution |
| title_sort | suboptimal chest radiography and artificial intelligence the problem and the solution |
| topic | artificial intelligence chest X-ray computer-assisted image processing quality improvement radiography |
| url | https://www.mdpi.com/2075-4418/13/3/412 |
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