{"id":3299,"date":"2024-08-28T06:50:04","date_gmt":"2024-08-28T06:50:04","guid":{"rendered":"https:\/\/www.radisentech.com\/?post_type=publication&p=3299"},"modified":"2025-03-27T09:31:08","modified_gmt":"2025-03-27T09:31:08","slug":"artificial-intelligence-assisted-pulmonary-tuberculosis-detection-from-chest-radiographs-a-facility-based-cross-sectional-study-at-a-tb-specialized-hospital-in-ethiopia","status":"publish","type":"publication","link":"https:\/\/www.radisentech.com\/en\/publication\/artificial-intelligence-assisted-pulmonary-tuberculosis-detection-from-chest-radiographs-a-facility-based-cross-sectional-study-at-a-tb-specialized-hospital-in-ethiopia\/","title":{"rendered":"Artificial Intelligence Assisted Pulmonary Tuberculosis Detection from Chest Radiographs: A Facility Based Cross-Sectional Study at a TB Specialized Hospital in Ethiopia"},"content":{"rendered":"\n
\n
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Published<\/h2>\n\n\n\n

Union Conference on Lung Health (2024)<\/p>\n\n\n\n

Authors<\/h2>\n\n\n\n

Metasebia Mesfin, M.D.1<\/sup>, Abraham Eshetu, M.D.1<\/sup>, Robel Gemechu1<\/sup>,
Abel Worku1<\/sup>, Desalegn Abebaw, Ph.D.2<\/sup>, Tariku Mengesha, M.D.1<\/sup><\/p>\n\n\n\n

Affiliations<\/h2>\n\n\n\n

1<\/sup>Kidus Petros Hospital, Addis Ababa, Ethiopia.
2<\/sup>Artificial Intelligence Engineering Division, RadiSen Co. Ltd., Seoul, Korea<\/em><\/p>\n<\/div>\n\n\n\n

\n

Summary<\/h2>\n\n\n\n

This study evaluates the detection performance of pulmonary Tuberculosis (TB) by a commercially available Artificial Intelligence (AI) software (AXIR-CX) from chest radiographs in Kidus Petros, a TB treatment hospital in Ethiopia. Prediction performances of the AI and expert radiologists are evaluated with the ground truth of Xpert MTB test results.<\/p>\n\n\n\n

Background<\/h2>\n\n\n\n

AI is increasingly embraced for detecting TB and related abnormalities worldwide. This study retrospectively evaluates the performance of AXIR-CX (version 2.5.0), an AI software developed by RadiSen in South Korea, for detecting TB using radiographs from Kidus Petros hospital.<\/p>\n\n\n\n

Methods<\/h2>\n\n\n\n

We collected Xpert results and chest radiographs of 1,579 (with 10% positives) individuals seen at the hospital in 2023. The AI\u2019s predictions were evaluated on the entire dataset. Subsequently, a subset of 321 radiographs (49% positives) were interpreted by three radiologists, two from South Korea and one from the hospital, with 10+ years of experience. The radiologists rated the radiographs for TB presence on a scale of 0 to 5, while the AI software provided probability values (0-100%). We compared predictive performances of the hospital\u2019s radiologist(without and with the help of AI), the joint South Korean radiologists, and the AI against Xpert results.<\/p>\n\n\n\n

Results<\/h2>\n\n\n\n

In the entire dataset, the AI predicted with sensitivity, specificity, and accuracy of 71%, 83%, and 82% respectively with an AUC of 0.83. In the sampled dataset of 321 cases, the joint radiologists (JointRads) demonstrated sensitivity, specificity, and accuracy of 54%, 95%, and 75% respectively while the AI achieved specificity and accuracy of 92% and 73% at a similar sensitivity level. The hospital\u2019s radiologist (Rad3) achieved sensitivity, specificity, and accuracy of 83%, 65%, and 74% respectively. At the same sensitivity level, the AI exhibited specificity and accuracy of 67% and 75%. When the radiologist was assisted by AI (Rad3&AI), a slight improvement was observed, with sensitivity, specificity, and accuracy reaching 85%, 68%, and 76% respectively.<\/p>\n\n\n\n

Conclusion<\/h2>\n\n\n\n

The AI demonstrated a capacity to identify tuberculosis with similar accuracy to skilled radiologists. Collaborative work between a radiologist and AI yields enhanced predictive performance. These findings indicate the potential usefulness of AI in hospital triage scenarios.<\/p>\n<\/div>\n<\/div>\n\n\n\n

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