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We categorised the computer vision platform into four technologies: image processing, object\/pattern recognition, computer graphics, and deep learning. In this paper, the focus is on image processing and deep learning (DL) applications for either 2D or 3D digitised human lung tissue images. This review is useful for establishing a common practice in TB analysis using human lung tissue as well as identifying opportunities for further research in this space. The review brings attention to the state-of-art techniques for detecting TB, with emphasis on the challenges and limitations of the current techniques. The ultimate goal is to promote the development of more efficient and accurate algorithms for the detection or analysis of TB, and raise awareness about the importance of early detection.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Design<\/jats:title>\n                <jats:p>We searched five databases and Google Scholar for articles published between January 2017 and December 2022 that focus on <jats:italic>Mycobacterium tuberculosis<\/jats:italic> detection, or <jats:italic>tuberculosis<\/jats:italic> pathology using digitised human lung tissue images. Details regarding design, image processing and computer-aided techniques, deep learning models, and datasets were collected and summarised. Discussions, analysis, and comparisons of state-of-the-art methods are provided to help guide future research. Further, a brief update on the relevant techniques and their performance is provided.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Several studies have been conducted to develop automated and AI-assisted methods for diagnosing <jats:italic>Mtb<\/jats:italic> and TB pathology from digitised human lung tissue images. Some studies presented a completely automated method of diagnosis, while other studies developed AI-assisted diagnostic methods. Low-level focus areas included the development of a novel <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\upmu$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mi>\u03bc<\/mml:mi>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula>CT scanner for soft tissue image contract, and use of multiresolution computed tomography to analyse the 3D structure of the human lung. High-level focus areas included the investigation the effects of aging on the number and size of small airways in the lungs using CT and whole lung high-resolution <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\upmu$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mi>\u03bc<\/mml:mi>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula>CT, and the 3D microanatomy characterisation of human <jats:italic>tuberculosis<\/jats:italic> lung using <jats:inline-formula><jats:alternatives><jats:tex-math>$$\\upmu$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mi>\u03bc<\/mml:mi>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula>CT in conjunction with histology and immunohistochemistry. Additionally, a novel method for acquiring high-resolution 3D images of human lung structure and topology is also presented.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>The literature indicates that post 1950s, TB was predominantly studied using animal models even though no animal model reflects the full spectrum of human pulmonary TB disease and does not reproducibly transmit <jats:italic>Mtb<\/jats:italic> infection to other animals (Hunter, 2011). This explains why there are very few studies that used human lung tissue for detection or analysis of <jats:italic>Mtb<\/jats:italic>. Nonetheless, we found 10 studies that used human tissues (predominately lung) of which five studies proposed machine learning (ML) models for the detection of <jats:italic>bacilli<\/jats:italic> and the other five used CT on human lung tissue scanned ex-vivo.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12880-024-01443-w","type":"journal-article","created":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T03:01:44Z","timestamp":1730775704000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Computer vision applications for the detection or analysis of tuberculosis using digitised human lung tissue images - a systematic review"],"prefix":"10.1186","volume":"24","author":[{"given":"Kapongo D.","family":"Lumamba","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gordon","family":"Wells","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Delon","family":"Naicker","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Threnesan","family":"Naidoo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adrie J. 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