Correlation between thoracic photographs and molecular rapid test (TCM) results of tuberculosis patients at the Lasinrang Pinrang Regional General Hospital
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Published:
Sep 30, 2025
Keywords:
Molecular Rapid Test (TCM), Thoracic Photo, Tuberculosis
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Abstract
Background: Tuberculosis is one of the deadliest infectious diseases in the world, Indonesia ranks third in the world with 824 thousand cases per year. Symptoms of Tuberculosis require a thoracic photograph-like examination with a molecular rapid test (TCM) examination. Although thoracic photographs are effective, they cannot detect infections in other organs, while TCM can diagnose more quickly at a higher cost. Objective: To determine the correlation between thoracic photographs and the results of the Molecular Rapid Test (TCM) of tuberculosis patients at the Lasinrang Pinrang Regional General Hospital. Research Methods: This study uses an observational analytical method with a cross sectional approach. The data used is secondary data, namely the patient's medical records. Research Results: Of the 100 patients studied at Lasinrang Pinrang Hospital, it was found that 80% showed infiltrate lesions on thoracic photographs, followed by 12% with consolidated lesions and 8% with fibrosis lesions. Molecular rapid test (TCM) results showed that 86% of patients were detected positive for Mycobacterium tuberculosis and sensitive to rifampicin, while 14% of TCM results were negative. Conclusion: The study showed that there was a significant relationship between the results of thoracic photo examination in the form of infiltrate lesions, consolidation, fibrosis and calcification to positive and negative results of molecular rapid test (TCM).
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Yusa, A. H. ., & Nasrum, S. N. F. . (2025). Correlation between thoracic photographs and molecular rapid test (TCM) results of tuberculosis patients at the Lasinrang Pinrang Regional General Hospital. Journal of Midwifery and Nursing, 7(3), 388-394. https://doi.org/10.35335/jmn.v7i3.6620
References
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Basem Abbas Al, U. (2018). The Radiological Diagnosis of Pulmonary Tuberculosis (TB) in Primary Care. Journal of Family Medicine and Disease Prevention, 4(1). https://doi.org/10.23937/2469-5793/1510073
Biewer, A., Tzelios, C., Calderón, R., Ríos, A., Gonzales, A., et al. (2024). Accuracy of digital chest x?ray analysis with qXR as a triage and screening tool in hospitalized patients being evaluated for tuberculosis in Lima, Peru. PLOS Global Public Health, 4(7), e0002752. https://doi.org/10.1371/journal.pgph.0002752
BMJ Open Respiratory Research Collaborative. (2024). Real?world clinical utility of Xpert MTB/RIF Ultra in assessment of tuberculosis and association with imaging?based measures of infectiousness. BMJ Open Respiratory Research, 11(1), e002624. https://doi.org/10.1136/bmjresp-2023-002624
Gelaw, S. M., Kik, S. V., Ruhwald, M., Ongarello, S., et al. (2022). Clinical evaluation of computer?aided digital x?ray detection of tuberculosis in prisons in the Americas. The Lancet Regional Health – Americas, 18, 100432. https://doi.org/10.1016/S2667-193X(22)00205-8
Irgi Ahmad Fahrezi, A., Irsandy, F., & Azka, H. (2024). Clinical Characteristics And Radiological Features Of Multi-Drug-Resistant (MDR) Pulmonary TB Patients. Jurnal Eduhealth, 15, 2024. https://doi.org/10.54209/eduhealth.v15i03
Kemenkes RI. (2023). Laporan Kinerja Kementerian Kesehatan Republik Indonesia Tahun 2023. Kementerian Kesehatan RI.
Kronik, G., Rsup, D., Kandou, R. D., Januari-Juni, P., Safitri, D. A., Mamesah, Y. P. M., & Timban, J. F. J. (2022). Gambaran Foto Toraks pada Pasien Tuberkulosis Paru dengan Penyakit Chest X-ray Profile of Lung Tuberculosis Patients with Chronic Kidney Disease at Prof. Medical Scope Journal, 4(1), 93–98. https://doi.org/10.35790/msj.v4.i1.44722
Kim, H.?J., Kwak, N., Yoon, S. H., Park, N., Kim, Y. R., Lee, J. H., Lee, J. Y., Park, Y., Kang, Y. A., Kim, S., Mok, J., Kim, J.?Y., Jeon, D., Lee, J.?K., & Yim, J.?J. (2024). Artificial intelligence?based radiographic extent analysis to predict tuberculosis treatment outcomes: A multicenter cohort study. Scientific Reports, 14, 13162. https://doi.org/10.1038/s41598-024-63885-0
Martin?Higuera, M. C., Rivas, G., Rolo, M., Muñoz?Gallego, I., & López?Roa, P. (2023). Xpert MTB/RIF Ultra CT value provides a rapid measure of sputum bacillary burden and predicts smear status in patients with pulmonary tuberculosis. Scientific Reports, 13, 1591. https://doi.org/10.1038/s41598-023-28869-6
Malherbe, S. T., et al. (2024). Clinical evaluation of computer?aided digital x?ray detection of tuberculosis in community active case finding. The Lancet Global Health, 12(5), e781–e793. https://doi.org/10.1016/S2214-109X(24)00516-3
Media Analis Kesehatan, J., Djasang, S., Hikmawati, E., Armah, Z., & Teknologi Laboratorium Medis Poltekkes Makassar, J. (2022). TINGKAT POSITIFITAS Mycobacterium tuberculosis Menggunakan Tcm Dengan Hasil Konversi Awal Pengobatan Short Regimen Pasien Tb MdR Positive Level Of Mycobacterium tuberculosis Using TCM With Initial Conversion Results Of Short Treatment Of MDR TB Patients. 13(1). https://doi.org/10.32382/mak.v13i1.2750
Migliori, G. B., Ong, C. W. M., Petrone, L., D’ambrosio, L., Centis, R., & Goletti, D. (2021). The definition of tuberculosis infection based on the spectrum of tuberculosis disease. In Breathe (Vol. 17, Issue 3). European Respiratory Society. https://doi.org/10.1183/20734735.0079-2021
Pramana, P. H. I., Dwija, I. B. N. P., & Hendrayana, M. A. (2021). Spesifisitas Dan Sensitivitas Pemeriksaan Mikroskopis Tbc Dibandingkan Pemeriksaan Kultur Tbc Pada Pasien Tuberkulosis Di Rumah Sakit Umum Pusat Sanglah. Jurnal Medika Udayana, 10.
Ruswandi, P. W. (2021). Perbedaan Gambaran Radiologis Penderita Tb Hiv Dengan Tes Cepat Molekuler (Tcm) Positif Dan Negatif.
Stop TB Partnership & icddr,b evaluation group. (2024). Independent evaluation of five AI CAD products for TB detection from chest x?rays using Xpert as the bacteriological reference standard. The Lancet Digital Health, 6(7), e501–e513. https://doi.org/10.1016/S2589-7500(24)00118-3
Sina, I., Kedokteran, J., Kedokteran, K.-F., Islam, U., Utara, S., Sikumbang, R. H., Penelitian, A., Chairani Eyanoer, P., Purnama Siregar, N., & Artikel, H. (2022). Faktor-Faktor Yang Berhubungan Dengan Kejadian Tb Paru Pada Usia Produktif Di Wilayah Kerja Puskesmas Tegal Sari Kecamatan Medan Denai Tahun 2018 Factors Related To The Incidence Of Pulmonary Tuberculosis At The Productive Age In The Working Area Of Tegal Sari Public Health Center In Medan Denai District In 2018. 21.
Susilawati, T. N., & Larasati, R. (2019). A recent update of the diagnostic methods for tuberculosis and their applicability in indonesia: A narrative review. In Medical Journal of Indonesia (Vol. 28, Issue 3, pp. 284–291). Faculty of Medicine, Universitas Indonesia. https://doi.org/10.13181/mji.v28i3.2589
Syafina, I. (2024). Evaluasi sensitivitas dan spesifisitas Xpert MTB RIF dan Xpert MTB RIF Ultra pada berbagai spesimen untuk mendiagnosis tuberkulosis ekstra paru: Sebuah telaah literatur sistematis. Holistik Jurnal Kesehatan, 18(3), 335–349. https://doi.org/10.33024/hjk.v18i3.166
Qin, Z. Z., Ahmed, S., Sarker, M. S., et al ( 2020). Can artificial intelligence (AI) be used to accurately detect tuberculosis (TB) from chest X-rays? An evaluation of five AI products for TB screening and triaging in a high TB burden setting. arXiv preprint.
Qin, Z. Z., Ahmed, S., Barrett, R., et al. (2024). Computer?aided detection of tuberculosis from chest radiographs in population screening: External validation and optimisation for field deployment. The Lancet Digital Health, 6(7), e488–e500. https://doi.org/10.1016/S2589-7500(24)00118-3
Wong, A., Lee, J. R. H., Rahmat-Khah, H., et al 2021. TB-Net: A Tailored, Self-Attention Deep Convolutional Neural Network Design for Detection of Tuberculosis Cases from Chest X-ray Images. arXiv preprint.
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