Publikasi Scopus 2024 per tanggal 31 Mei 2024 (409 artikel)

298

Authors

Tenda E.D.; Yunus R.E.; Zulkarnaen B.; Yugo M.R.; Pitoyo C.W.; Asaf M.M.; Islamiyati T.N.; Pujitresnani A.; Setiadharma A.; Henrina J.; Rumende C.M.; Wulani V.; Harimurti K.; Lydia A.; Shatri H.; Soewondo P.; Yusuf P.A.

Author_Full_Names

Tenda, Eric Daniel (57189692101); Yunus, Reyhan Eddy (57215658457); Zulkarnaen, Benny (59001798900); Yugo, Muhammad Reynalzi (59001799000); Pitoyo, Ceva Wicaksono (26022606900); Asaf, Moses Mazmur (57216406843); Islamiyati, Tiara Nur (59001799100); Pujitresnani, Arierta (58289758500); Setiadharma, Andry (58837878100); Henrina, Joshua (57218482646); Rumende, Cleopas Martin (14325966300); Wulani, Vally (55980673700); Harimurti, Kuntjoro (23473513200); Lydia, Aida (8451287200); Shatri, Hamzah (2876

Author S Id

57189692101; 57215658457; 59001798900; 59001799000; 26022606900; 57216406843; 59001799100; 58289758500; 58837878100; 57218482646; 14325966300; 55980673700; 23473513200; 8451287200; 28767986500; 23475336100; 57192156597

Title

Comparison of the Discrimination Performance of AI Scoring and the Brixia Score in Predicting COVID-19 Severity on Chest X-Ray Imaging: Diagnostic Accuracy Study

Year

2024

Source Title

JMIR Formative Research

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Cited By

Link

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191344783&doi=10.2196%2f46817&partnerID=40&md5=0a86b650102546412f87cf24f9775376

Affiliations

Department of Internal Medicine, Pulmonology and Critical Care Division, Faculty of Medicine Universitas Indonesia, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia; Department of Radiology, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia; Department of Medical Physiology and Biophysics, Medical Technology Cluster IMERI, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia; Department of Internal Medicine, Geriatric Division, Faculty of Medicine Universitas Indonesia, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia; Department of Internal Medicine, Nephrology and Hypertension Division, Faculty of Medicine Universitas Indonesia, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia; Department of Internal Medicine, Psychosomatic Division, Faculty of Medicine Universitas Indonesia, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia; Department of Internal Medicine, Endocrinology - Metabolism - Diabetes Division, Faculty of Medicine Universitas Indonesia, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia

Authors With Affiliations

Tenda E.D., Department of Internal Medicine, Pulmonology and Critical Care Division, Faculty of Medicine Universitas Indonesia, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia; Yunus R.E., Department of Radiology, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia; Zulkarnaen B., Department of Radiology, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia; Yugo M.R., Department of Radiology, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia; Pitoyo C.W., Department of Internal Medicine, Pulmonology and Critical Care Division, Faculty of Medicine Universitas Indonesia, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia; Asaf M.M., Department of Radiology, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia; Islamiyati T.N., Department of Internal Medicine, Pulmonology and Critical Care Division, Faculty of Medicine Universitas Indonesia, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia; Pujitresnani A., Department of Medical Physiology and Biophysics, Medical Technology Cluster IMERI, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia; Setiadharma A., Department of Internal Medicine, Pulmonology and Critical Care Division, Faculty of Medicine Universitas Indonesia, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia; Henrina J., Department of Internal Medicine, Pulmonology and Critical Care Division, Faculty of Medicine Universitas Indonesia, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia; Rumende C.M., Department of Internal Medicine, Pulmonology and Critical Care Division, Faculty of Medicine Universitas Indonesia, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia; Wulani V., Department of Radiology, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia; Harimurti K., Department of Internal Medicine, Geriatric Division, Faculty of Medicine Universitas Indonesia, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia; Lydia A., Department of Internal Medicine, Nephrology and Hypertension Division, Faculty of Medicine Universitas Indonesia, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia; Shatri H., Department of Internal Medicine, Psychosomatic Division, Faculty of Medicine Universitas Indonesia, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia; Soewondo P., Department of Internal Medicine, Endocrinology - Metabolism - Diabetes Division, Faculty of Medicine Universitas Indonesia, RSUPN Dr. Cipto Mangunkusumo, Universitas Indonesia, Jakarta, Indonesia; Yusuf P.A., Department of Medical Physiology and Biophysics, Medical Technology Cluster IMERI, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia

Abstract

Background: The artificial intelligence (AI) analysis of chest x-rays can increase the precision of binary COVID-19 diagnosis. However, it is unknown if AI-based chest x-rays can predict who will develop severe COVID-19, especially in low- and middle-income countries. Objective: The study aims to compare the performance of human radiologist Brixia scores versus 2 AI scoring systems in predicting the severity of COVID-19 pneumonia. Methods: We performed a cross-sectional study of 300 patients suspected with and with confirmed COVID-19 infection in Jakarta, Indonesia. A total of 2 AI scores were generated using CAD4COVID x-ray software. Results: The AI probability score had slightly lower discrimination (area under the curve [AUC] 0.787, 95% CI 0.722-0.852). The AI score for the affected lung area (AUC 0.857, 95% CI 0.809-0.905) was almost as good as the human Brixia score (AUC 0.863, 95% CI 0.818-0.908). Conclusions: The AI score for the affected lung area and the human radiologist Brixia score had similar and good discrimination performance in predicting COVID-19 severity. Our study demonstrated that using AI-based diagnostic tools is possible, even in low-resource settings. However, before it is widely adopted in daily practice, more studies with a larger scale and that are prospective in nature are needed to confirm our findings. © 2024 JMIR Publications Inc.. All rights reserved.

Author Keywords

AI scoring system; artificial intelligence; artificial intelligence scoring system; Brixia; CAD4COVID; chest x-ray; COVID-19; disease severity; pneumonia; prediction; radiograph

Index Keywords

Funding Details

Universitas Indonesia, UI, (PUTI Q1 2022, NKB-429/UN2, RST/HKP.05.00/2022)

Funding Text

The authors thank Delft Imaging for providing CAD4COVID free of charge. The research team institutions have also signed a joint research arrangement between Universitas Indonesia, Dr. Cipto Mangunkusumo National Referral Hospital, and Delft Imaging System, encompassing rights and obligations for each party on data confidentiality during and after the research period. This study was supported by Un

Publisher

JMIR Publications Inc.

Issn

2561326X

Isbn

Pubmed Id

Document Type

Article

Sjr Best Quartile

Sjr

592

Sjr Rank

8714