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K SP, Parivakkam Mani A, S G, Yadav S. Advancements in Artificial Intelligence for the Diagnosis of Multidrug Resistance and Extensively Drug-Resistant Tuberculosis: A Comprehensive Review. Cureus 2024; 16:e60280. [PMID: 38872656 PMCID: PMC11173349 DOI: 10.7759/cureus.60280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/11/2024] [Indexed: 06/15/2024] Open
Abstract
Tuberculosis (TB) remains a significant global health concern, particularly with the emergence of multidrug-resistant tuberculosis (MDR-TB) and extensively drug-resistant tuberculosis (XDR-TB). Traditional methods for diagnosing drug resistance in TB are time-consuming and often lack accuracy, leading to delays in appropriate treatment initiation and exacerbating the spread of drug-resistant strains. In recent years, artificial intelligence (AI) techniques have shown promise in revolutionizing TB diagnosis, offering rapid and accurate identification of drug-resistant strains. This comprehensive review explores the latest advancements in AI applications for the diagnosis of MDR-TB and XDR-TB. We discuss the various AI algorithms and methodologies employed, including machine learning, deep learning, and ensemble techniques, and their comparative performances in TB diagnosis. Furthermore, we examine the integration of AI with novel diagnostic modalities such as whole-genome sequencing, molecular assays, and radiological imaging, enhancing the accuracy and efficiency of TB diagnosis. Challenges and limitations surrounding the implementation of AI in TB diagnosis, such as data availability, algorithm interpretability, and regulatory considerations, are also addressed. Finally, we highlight future directions and opportunities for the integration of AI into routine clinical practice for combating drug-resistant TB, ultimately contributing to improved patient outcomes and enhanced global TB control efforts.
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Affiliation(s)
- Shanmuga Priya K
- Department of Pulmonology, Faculty of Medicine, Sri Lalithambigai Medical College and Hospital, Dr MGR Educational and Research Institute, Chennai, IND
| | - Anbumaran Parivakkam Mani
- Department of Respiratory Medicine, Saveetha Medical College and Hospital, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, IND
| | - Geethalakshmi S
- Department of Microbiology, Sri Lalithambigai Medical College and Hospital, Dr MGR Educational and Research Institute, Chennai, IND
| | - Sankalp Yadav
- Department of Medicine, Shri Madan Lal Khurana Chest Clinic, New Delhi, IND
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Fang WJ, Tang SN, Liang RY, Zheng QT, Yao DQ, Hu JX, Song M, Zheng GP, Rosenthal A, Tartakovsky M, Lu PX, Wáng YXJ. Differences in pulmonary nodular consolidation and pulmonary cavity among drug-sensitive, rifampicin-resistant and multi-drug resistant tuberculosis patients: the Guangzhou computerized tomography study. Quant Imaging Med Surg 2024; 14:1010-1021. [PMID: 38223080 PMCID: PMC10783999 DOI: 10.21037/qims-23-694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 10/28/2023] [Indexed: 01/16/2024]
Abstract
Background Pulmonary nodular consolidation (PN) and pulmonary cavity (PC) may represent the two most promising imaging signs in differentiating multidrug-resistant (MDR)-pulmonary tuberculosis (PTB) from drug-sensitive (DS)-PTB. However, there have been concerns that literature described radiological feature differences between DS-PTB and MDR-PTB were confounded by that MDR-PTB cases tend to have a longer history. This study seeks to further clarify this point. Methods All cases were from the Guangzhou Chest Hospital, Guangzhou, China. We retrieved data of consecutive new MDR cases [n=46, inclusive of rifampicin-resistant (RR) cases] treated during the period of July 2020 and December 2021, and according to the electronic case archiving system records, the main PTB-related symptoms/signs history was ≤3 months till the first computed tomography (CT) scan in Guangzhou Chest Hospital was taken. To pair the MDR-PTB cases with assumed equal disease history length, we additionally retrieved data of 46 cases of DS-PTB patients. Twenty-two of the DS patients and 30 of the MDR patients were from rural communities. The first CT in Guangzhou Chest Hospital was analysed in this study. When the CT was taken, most cases had anti-TB drug treatment for less than 2 weeks, and none had been treated for more than 3 weeks. Results Apparent CT signs associated with chronicity were noted in 10 cases in the DS group (10/46) and 9 cases in the MDR group (10/46). Thus, the overall disease history would have been longer than the assumed <3 months. Still, the history length difference between DS patients and MDR patients in the current study might not be substantial. The lung volume involvement was 11.3%±8.3% for DS cases and 8.4%±6.6% for MDR cases (P=0.022). There was no statistical difference between DS cases and MDR cases both in PN prevalence and in PC prevalence. For positive cases, MDR cases had more PN number (mean of positive cases: 2.63 vs. 2.28, P=0.38) and PC number (mean of positive cases: 2.14 vs. 1.38, P=0.001) than DS cases. Receiver operating characteristic curve analysis shows, PN ≥4 and PC ≥3 had a specificity of 86% (sensitivity 25%) and 93% (sensitivity 36%), respectively, in suggesting the patient being a MDR cases. Conclusions A combination of PN and PC features allows statistical separation of DS and MDR cases.
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Affiliation(s)
- Wei-Jun Fang
- Department of Radiology, Guangzhou Chest Hospital, State Key Laboratory of Respiratory Disease, Guangzhou, China
| | - Sheng-Nan Tang
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Rui-Yun Liang
- Department of Radiology, Guangzhou Chest Hospital, State Key Laboratory of Respiratory Disease, Guangzhou, China
| | - Qiu-Ting Zheng
- Department of Medical Imaging, Shenzhen Center for Chronic Disease Control, Shenzhen, China
| | - Dian-Qi Yao
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
| | - Jin-Xing Hu
- Department of Tuberculosis, Guangzhou Chest Hospital, State Key Laboratory of Respiratory Disease, Guangzhou, China
| | - Min Song
- Department of Radiology, Guangzhou Chest Hospital, State Key Laboratory of Respiratory Disease, Guangzhou, China
| | - Guang-Ping Zheng
- Department of Radiology, The Third People’s Hospital of Shenzhen, Shenzhen, China
| | - Alex Rosenthal
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA
| | - Michael Tartakovsky
- Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA
| | - Pu-Xuan Lu
- Department of Medical Imaging, Shenzhen Center for Chronic Disease Control, Shenzhen, China
| | - Yì Xiáng J. Wáng
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China
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Feyisa DW, Ayano YM, Debelee TG, Schwenker F. Weak Localization of Radiographic Manifestations in Pulmonary Tuberculosis from Chest X-ray: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:6781. [PMID: 37571564 PMCID: PMC10422452 DOI: 10.3390/s23156781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 07/03/2023] [Accepted: 07/14/2023] [Indexed: 08/13/2023]
Abstract
Pulmonary tuberculosis (PTB) is a bacterial infection that affects the lung. PTB remains one of the infectious diseases with the highest global mortalities. Chest radiography is a technique that is often employed in the diagnosis of PTB. Radiologists identify the severity and stage of PTB by inspecting radiographic features in the patient's chest X-ray (CXR). The most common radiographic features seen on CXRs include cavitation, consolidation, masses, pleural effusion, calcification, and nodules. Identifying these CXR features will help physicians in diagnosing a patient. However, identifying these radiographic features for intricate disorders is challenging, and the accuracy depends on the radiologist's experience and level of expertise. So, researchers have proposed deep learning (DL) techniques to detect and mark areas of tuberculosis infection in CXRs. DL models have been proposed in the literature because of their inherent capacity to detect diseases and segment the manifestation regions from medical images. However, fully supervised semantic segmentation requires several pixel-by-pixel labeled images. The annotation of such a large amount of data by trained physicians has some challenges. First, the annotation requires a significant amount of time. Second, the cost of hiring trained physicians is expensive. In addition, the subjectivity of medical data poses a difficulty in having standardized annotation. As a result, there is increasing interest in weak localization techniques. Therefore, in this review, we identify methods employed in the weakly supervised segmentation and localization of radiographic manifestations of pulmonary tuberculosis from chest X-rays. First, we identify the most commonly used public chest X-ray datasets for tuberculosis identification. Following that, we discuss the approaches for weakly localizing tuberculosis radiographic manifestations in chest X-rays. The weakly supervised localization of PTB can highlight the region of the chest X-ray image that contributed the most to the DL model's classification output and help pinpoint the diseased area. Finally, we discuss the limitations and challenges of weakly supervised techniques in localizing TB manifestations regions in chest X-ray images.
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Affiliation(s)
- Degaga Wolde Feyisa
- Ethiopian Artificial Intelligence Institute, Addis Ababa P.O. Box 40782, Ethiopia; (D.W.F.); (Y.M.A.); (T.G.D.)
| | - Yehualashet Megersa Ayano
- Ethiopian Artificial Intelligence Institute, Addis Ababa P.O. Box 40782, Ethiopia; (D.W.F.); (Y.M.A.); (T.G.D.)
| | - Taye Girma Debelee
- Ethiopian Artificial Intelligence Institute, Addis Ababa P.O. Box 40782, Ethiopia; (D.W.F.); (Y.M.A.); (T.G.D.)
- Department of Electrical and Computer Engineering, Addis Ababa Science and Technology University, Addis Ababa P.O. Box 120611, Ethiopia
| | - Friedhelm Schwenker
- Institute of Neural Information Processing, Ulm University, 89069 Ulm, Germany
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