Post-deployment performance of a deep learning algorithm for normal and abnormal chest X-ray classification: A study at visa screening centers in the United Arab Emirates
Background: Chest radiographs (CXRs) are widely used to screen for infectious diseases like tuberculosis and COVID-19 among migrants. At such high-volume settings, manual CXR reporting is challenging and integrating artificial intelligence (AI) algorithms into the workflow help to rule out normal fi...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | European Journal of Radiology Open |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352047724000613 |
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| author | Amina Abdelqadir Mohamed AlJasmi Hatem Ghonim Mohyi Eldin Fahmy Aswathy Nair Shamie Kumar Dennis Robert Afrah Abdikarim Mohamed Hany Abdou Anumeha Srivastava Bhargava Reddy |
| author_facet | Amina Abdelqadir Mohamed AlJasmi Hatem Ghonim Mohyi Eldin Fahmy Aswathy Nair Shamie Kumar Dennis Robert Afrah Abdikarim Mohamed Hany Abdou Anumeha Srivastava Bhargava Reddy |
| author_sort | Amina Abdelqadir Mohamed AlJasmi |
| collection | DOAJ |
| description | Background: Chest radiographs (CXRs) are widely used to screen for infectious diseases like tuberculosis and COVID-19 among migrants. At such high-volume settings, manual CXR reporting is challenging and integrating artificial intelligence (AI) algorithms into the workflow help to rule out normal findings in minutes, allowing radiologists to focus on abnormal cases. Methods: In this post-deployment study, all the CXRs acquired during the visa screening process across 33 centers in United Arab Emirates from January 2021 to June 2022 (18 months) were included. The qXR v2.1 chest X-ray interpretation software was used to classify the scans into normal and abnormal, and its agreement against radiologist was evaluated. Additionally, a digital survey was conducted among 20 healthcare professionals with prior AI experience to understand real-world implementation challenges and impact. Results: The analysis of 1309,443 CXRs from 1309,431 patients (median age: 35 years; IQR [29–42]; 1030,071 males [78.7 %]) in this study revealed a Negative Predictive Value (NPV) of 99.92 % (95 % CI: 99.92, 99.93), Positive Predictive Value (PPV) of 5.06 % (95 % CI: 4.99, 5.13) and overall percent agreement of the AI with radiologists of 72.90 % (95 % CI: 72.82, 72.98). In the survey, majority (88.2 %) of the radiologists agreed to turnaround time reduction after AI integration, while 82 % suggested that the AI improved their diagnostic accuracy. Discussion: In contrast with the existing studies, this research uses a substantially large data. A high NPV and satisfactory agreement with human readers indicate that AI can reliably identify normal CXRs, making it suitable for routine applications. |
| format | Article |
| id | doaj-art-fbcaf92bcf6f4b19a639e38a043165f5 |
| institution | Kabale University |
| issn | 2352-0477 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | European Journal of Radiology Open |
| spelling | doaj-art-fbcaf92bcf6f4b19a639e38a043165f52024-12-15T06:15:49ZengElsevierEuropean Journal of Radiology Open2352-04772024-12-0113100606Post-deployment performance of a deep learning algorithm for normal and abnormal chest X-ray classification: A study at visa screening centers in the United Arab EmiratesAmina Abdelqadir Mohamed AlJasmi0Hatem Ghonim1Mohyi Eldin Fahmy2Aswathy Nair3Shamie Kumar4Dennis Robert5Afrah Abdikarim Mohamed6Hany Abdou7Anumeha Srivastava8Bhargava Reddy9Emirates Health Services, DSO Digital Park Building A8, Dubai Silicon Oasis, Dubai, UAEUnison Capital Investment LLC, Park Heights Square, Dubai Hills Estate, UAEUnison Capital Investment LLC, Park Heights Square, Dubai Hills Estate, UAEQure.ai Technologies Pvt Ltd, Prestige Summit, 6, St Johns Rd, Halasuru, Bengaluru, India; Correspondence to: Qure.ai, Bengaluru, Karnataka, India.Qure.ai Technologies Pvt Ltd, Prestige Summit, 6, St Johns Rd, Halasuru, Bengaluru, IndiaQure.ai Technologies Pvt Ltd, Prestige Summit, 6, St Johns Rd, Halasuru, Bengaluru, IndiaEmirates Health Services, DSO Digital Park Building A8, Dubai Silicon Oasis, Dubai, UAEUnison Capital Investment LLC, Park Heights Square, Dubai Hills Estate, UAEQure.ai Technologies Pvt Ltd, Prestige Summit, 6, St Johns Rd, Halasuru, Bengaluru, IndiaQure.ai Technologies Pvt Ltd, Prestige Summit, 6, St Johns Rd, Halasuru, Bengaluru, IndiaBackground: Chest radiographs (CXRs) are widely used to screen for infectious diseases like tuberculosis and COVID-19 among migrants. At such high-volume settings, manual CXR reporting is challenging and integrating artificial intelligence (AI) algorithms into the workflow help to rule out normal findings in minutes, allowing radiologists to focus on abnormal cases. Methods: In this post-deployment study, all the CXRs acquired during the visa screening process across 33 centers in United Arab Emirates from January 2021 to June 2022 (18 months) were included. The qXR v2.1 chest X-ray interpretation software was used to classify the scans into normal and abnormal, and its agreement against radiologist was evaluated. Additionally, a digital survey was conducted among 20 healthcare professionals with prior AI experience to understand real-world implementation challenges and impact. Results: The analysis of 1309,443 CXRs from 1309,431 patients (median age: 35 years; IQR [29–42]; 1030,071 males [78.7 %]) in this study revealed a Negative Predictive Value (NPV) of 99.92 % (95 % CI: 99.92, 99.93), Positive Predictive Value (PPV) of 5.06 % (95 % CI: 4.99, 5.13) and overall percent agreement of the AI with radiologists of 72.90 % (95 % CI: 72.82, 72.98). In the survey, majority (88.2 %) of the radiologists agreed to turnaround time reduction after AI integration, while 82 % suggested that the AI improved their diagnostic accuracy. Discussion: In contrast with the existing studies, this research uses a substantially large data. A high NPV and satisfactory agreement with human readers indicate that AI can reliably identify normal CXRs, making it suitable for routine applications.http://www.sciencedirect.com/science/article/pii/S2352047724000613Chest radiographAbnormalityVisa screeningArtificial intelligenceWorkflowAgreement |
| spellingShingle | Amina Abdelqadir Mohamed AlJasmi Hatem Ghonim Mohyi Eldin Fahmy Aswathy Nair Shamie Kumar Dennis Robert Afrah Abdikarim Mohamed Hany Abdou Anumeha Srivastava Bhargava Reddy Post-deployment performance of a deep learning algorithm for normal and abnormal chest X-ray classification: A study at visa screening centers in the United Arab Emirates European Journal of Radiology Open Chest radiograph Abnormality Visa screening Artificial intelligence Workflow Agreement |
| title | Post-deployment performance of a deep learning algorithm for normal and abnormal chest X-ray classification: A study at visa screening centers in the United Arab Emirates |
| title_full | Post-deployment performance of a deep learning algorithm for normal and abnormal chest X-ray classification: A study at visa screening centers in the United Arab Emirates |
| title_fullStr | Post-deployment performance of a deep learning algorithm for normal and abnormal chest X-ray classification: A study at visa screening centers in the United Arab Emirates |
| title_full_unstemmed | Post-deployment performance of a deep learning algorithm for normal and abnormal chest X-ray classification: A study at visa screening centers in the United Arab Emirates |
| title_short | Post-deployment performance of a deep learning algorithm for normal and abnormal chest X-ray classification: A study at visa screening centers in the United Arab Emirates |
| title_sort | post deployment performance of a deep learning algorithm for normal and abnormal chest x ray classification a study at visa screening centers in the united arab emirates |
| topic | Chest radiograph Abnormality Visa screening Artificial intelligence Workflow Agreement |
| url | http://www.sciencedirect.com/science/article/pii/S2352047724000613 |
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