The utility of artificial intelligence in identifying radiological evidence of lung cancer and pulmonary tuberculosis in a high-burden tuberculosis setting

Authors

DOI:

https://doi.org/10.7196/SAMJ.2024.v114i6.1846

Keywords:

Utility, artificial intelligence, pulmonary tuberculosis, lung cancer, computer aided detection.

Abstract

Background. Artificial intelligence (AI), using deep learning (DL) systems, can be utilised to detect radiological changes of various pulmonary diseases. Settings with a high burden of tuberculosis (TB) and people living with HIV can potentially benefit from the use of AI to augment resource-constrained healthcare systems.

Objective. To assess the utility of qXR software (AI) in detecting radiological changes compatible with lung cancer or pulmonary TB (PTB).

Methods. We performed an observational study in a tertiary institution that serves a population with a high burden of lung cancer and PTB. In total, 382 chest radiographs that had a confirmed diagnosis were assessed: 127 with lung cancer, 144 with PTB and 111 normal. These chest radiographs were de-identified and randomly uploaded by a blinded investigator into qXR software. The output was generated as probability scores from predefined threshold values.

Results. The overall sensitivity of the qXR in detecting lung cancer was 84% (95% confidence interval (CI) 80 - 87%), specificity 91% (95% CI 84 - 96%) and positive predictive value of 97% (95% CI 95 - 99%). For PTB, it had a sensitivity of 90% (95% CI 87 - 93%) and specificity of 79% (95% CI 73 - 84%) and negative predictive value of 85% (95% CI 79 - 91%).


Conclusion. The qXR software was sensitive and specific in categorising chest radiographs as consistent with lung cancer or TB, and can potentially aid in the earlier detection and management of these diseases.

Author Biographies

  • Z Z Nxumalo, Division of Pulmonology, Department of Medicine, Faculty of Medicine and Health Sciences, Stellenbosch University and Tygerberg Hospital, Cape Town, South Africa

    Division of Pulmonology, Department of Medicine and Health Sciences, Stellenbosch University and Tygerberg Hospital, Cape Town, South Africa

    Medical registrar

    MBCHB, DipHIVman

  • E M Irusen, Division of Pulmonology, Department of Medicine, Faculty of Medicine and Health Sciences, Stellenbosch University and Tygerberg Hospital, Cape Town, South Africa

    Division of Pulmonology, Department of Medicine and Health Sciences, Stellenbosch University and Tygerberg Hospital, Cape Town, South Africa

    MBChB (Natal), FCP (SA), Cert Pulm (SA), PhD (Natal)

  • B W Allwood, Division of Pulmonology, Department of Medicine, Faculty of Medicine and Health Sciences, Stellenbosch University and Tygerberg Hospital, Cape Town, South Africa

    Division of Pulmonology, Department of Medicine, Stellenbosch University and Tygerberg Hospital, Cape Town, South Africa 

    Consultant/senior specialist

    MBBCh (Wits) DCH (SA) DA(SA) FCP(SA) MPH(UCT) Cert Pulm(SA) PhD(UCT)

  • M Tadepalli, Qure.ai, Mumbai, India

    Bachelor of Technology in Computer Science, IIT Bombay.

  • J Bassi, Qure.ai, Mumbai, India

    Bachelor of Engineering (Hons) in Chemical Engineering

  • C F N Koegelenberg, Division of Pulmonology, Department of Medicine, Faculty of Medicine and Health Sciences, Stellenbosch University and Tygerberg Hospital, Cape Town, South Africa

    Head of the Division of Pulmonology

    MBChB (SU), MMed (Int), FCP (SA), FRCP (UK), Cert Pulm (SA), PhD

    Division of Pulmonology, Department of Medicine and Health Sciences, Stellenbosch University and Tygerberg Hospital, Cape Town, South Africa

References

Nam JG, Hwang EJ, Kim J, et al. AI improves nodule detection on chest radiographs in a health screening population: A randomised controlled trial. Radiology 2023;307(2):e221894. https://doi. org/10.1148/radiol.221894

Rajpurkar P, Lungren MP. The current and future state of AI interpretation of medical images. N Engl J Med 2023;388(21):1981-1990. https://doi.org/10.1056/NEJMra2301725

Van Zyl Smit RN, Pai M, et al. Global lung health: The colliding epidemics of tuberculosis, tobacco smoking, HIV and COPD. Eur Respir J 2010;35(1):27-33. https://doi.org/10.1183/09031936.00072909 4. Barta JA, Powell CA, Wisnivesky JP. Global epidemiology of lung cancer. Ann Glob Health

;85(1):8. https://doi.org/10.5334/aogh.2419

Al Lehebe A, Alomair A, Mahboub B, et al. Recommended approaches for screening and early

detection of lung cancer in the Middle East and Africa (MEA) region: A consensus statement. J Thorac

Dis 2024;16(3):2142-2158 https://doi: 10.21037/jtd-23-1568

World Health Organization.Global tuberculosis report WHO 2022. Geneva: WHO, 2022. https://

www.who.int/teams/global-tuberculosis-programme/tb-reports/global-tuberculosis-report-2022/tb-

disease-burden/2-1-tb-incidence (accessed 20 February 2023).

Putha P, Tadepalli M, Reddy B, et al. Can artificial intelligence reliably report chest X-rays?: Radiologist

validation of an algorithm trained on 2.3 million X-rays. arxiv:1807.07455, 2018. https://doi.

org/10.48550/arXiv.1807.07455

Mahboub B, Tadepalli M, Raj T, et al. Identifying malignant nodules on chest X-rays: A validation study of radiologist versus artificial intelligence diagnostic accuracy. Adv Biomed Health Sci 2022;1:137-143. https://doi.org/10.4103/abhs.abhs_17_22

Melendez J, Sánchez CI, Philipsen RH, et al. An automated tuberculosis screening strategy combining X-ray-based computer-aided detection and clinical information. Sci Rep 2016;6:25265. https://doi. org/10.1038/srep25265

Qin ZZ, Ahmed S, Sarker MS, et al. Tuberculosis detection from chest X-rays for triaging in a high tuberculosis-burden setting: An evaluation of five artificial intelligence algorithms. Lancet Digit Health 2021;3(9):e543-e554. https://doi.org/10.1016/S2589-7500(21)00116-3

World Health Organization. High priority target product profiles for new tuberculosis diagnostics: Report of a consensus meeting, 28 -29 April 2014. Geneva: WHO, 2014. https://www.who.int/ publications/i/item/WHO-HTM-TB-2014.18 (accessed 15 January 2022).

Vos T, Allen C, Arora M, et al. Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990 - 2015: A systematic analysis for the Global Burden of Disease Study 2015. Lancet 2016;388(10053):1545-1602. https://doi.org/10.1016/S0140- 6736(16)31678-6

Tandon YK, Bartholmai BJ, Koo CW. Putting artificial intelligence (AI) on the spot: Machine learning evaluation of pulmonary nodules. J Thorac Dis 2020;12(11):6954-6965. https://doi.org/10.21037/jtd- 2019-cptn-03

Berle DR, DeMello S, Berg CD, et al; National Lung Screening Trial Research Team. Results of the two incidence screenings in the National Lung Screening Trial. N Engl J Med 2013;369(10):920-931. https://doi.org/10.1056/NEJMoa1208962

Yoo H, Kim KH, Singh R, Digumarthy SR, Kalra MK. Validation of a deep learning algorithm for the detection of malignant pulmonary nodules in chest radiographs. JAMA Netw Open 2020;3(9):e2017135. https://doi.org/10.1001/jamanetworkopen.2020.17135

Singh R, Kalra MK, Nitiwarangkul C, et al. Deep learning in chest radiography: Detection of findings and presence of change. PLoS ONE 2018;13(10):e0204155. https://doi.org/10.1371/journal. pone.0204155

Homayounieh F, Digumarthy S, Ebrahimian S, et al. An artificial intelligence-based chest X-ray model on human nodule detection accuracy from a multicenter study. JAMA Netw Open 2021;4(12):e2141096. https://doi.org/10.1001/jamanetworkopen.2021.41096

Downloads

Published

2024-05-31

Issue

Section

Research

How to Cite

1.
Nxumalo ZZ, Irusen EM, Allwood BW, Tadepalli M, Bassi J, Koegelenberg CFN. The utility of artificial intelligence in identifying radiological evidence of lung cancer and pulmonary tuberculosis in a high-burden tuberculosis setting. S Afr Med J [Internet]. 2024 May 31 [cited 2025 Jan. 16];114(6):e1846. Available from: https://samajournals.co.za/index.php/samj/article/view/1846

Similar Articles

1-10 of 76

You may also start an advanced similarity search for this article.