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Mar 23, 2023
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Jun 5, 2023
Published
Jun 27, 2023
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Abstract

Background: Another chronic problem in tuberculosis (TB) eradication programs amid the widespread use of artificial intelligence (AI) in the health world is that early identification of the healing phase has not been found using similar technology. The purpose is to find if the transformation concept of AI will be able to help identify symptoms of early recovery of TB.


Methods: This research used a document review with a descriptive design. Data was processed by PRISMA analysis. The keywords were Artificial Intelligence, convalescence phase, and tuberculosis. The data were filtered from Google Engine included from Google Scholar, ResearchGate, PubMed, and Semantic Scholar screened in the last 5 years (2018-2023), in English or Indonesian. The stages of document screening were adjusted to the PRISMA diagram, and were analyzed descriptively. Results: The study shows that the majority of AI studies discussed diagnosis (n=9 or 69.2%), only 3 documents (23.1%) discussed on TB treatment, and 1 document (7.7%) on monitoring. In conclusion, early identification of the recovery phase of TB patients is supported by previous researchers and can be done in the form of an application.


Conclusions: Artificial intelligence in the TB eradication program has value especially if conducted integrated with other health programs.

Keywords

Artificial Intelligence convalescence phase tuberculosis

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How to Cite
Sukatemin, S., & Peristiowati, Y. (2023). Transformation Concept of Artificial Intelligencein the Early Identification of Tuberculosis Recovery Phase: A Systematic Literature Review: Early Identification of Tuberculosis Recovery. INDONESIAN JOURNAL OF HEALTH SCIENCES RESEARCH AND DEVELOPMENT (IJHSRD), 5(1), 30–41. https://doi.org/10.36566/ijhsrd/Vol5.Iss1/144

References

  1. Juanamasta IG, Iblasi AS, Aungsuroch Y, Yunibhand J. Nursing Development in Indonesia : Colonialism , After Independence and Nursing act. 2021;(662).
  2. Pedro Dos Reis F, Amaro R, Martins Silva F, Vaz Pinto S, Barroca I, Sá T, et al. The impact of confinement on children and adolescents. Acta Med Port. 2021;34(4):245–6.
  3. Setyowati L, Sukmawan S, El-Sulukkiyah AA. Learning from home during pandemic: A blended learning for reading to write activity in EFL setting. JEES (Journal English Educ Soc. 2021;6(1):9–17.
  4. Subedi S, Nayaju S, Subedi S, Shah SK, Shah JM. Impact of E-learning during COVID-19 Pandemic among Nursing Students and Teachers of Nepal. Int J Sci Healthc Res [Internet]. 2020;5(3):68–76. Available from: www.ijshr.com
  5. Sulastri, Yuniartika W, Triana DAA, Giyoto. Transformation of the learning system in nursing education after the COVID-19 pandemic. Bali Med J. 2022;11(3):1675–80.
  6. Sahu S, Ditiu L, Sachdeva KS, Zumla A. Recovering from the Impact of the Covid-19 Pandemic and Accelerating to Achieving the United Nations General Assembly Tuberculosis Targets. Int J Infect Dis [Internet]. 2021;113(2021):S100–3. Available from: https://doi.org/10.1016/j.ijid.2021.02.078
  7. Agberotimi SF, Akinsola OS, Oguntayo R, Olaseni AO. Interactions Between Socioeconomic Status and Mental Health Outcomes in the Nigerian Context Amid COVID-19 Pandemic: A Comparative Study. Front Psychol. 2020;11(January):1–7.
  8. Ros M, Neuwirth LS. Increasing global awareness of timely COVID-19 healthcare guidelines through FPV training tutorials: Portable public health crises teaching method. Nurse Educ Today [Internet]. 2020;91(May):104479. Available from: https://doi.org/10.1016/j.nedt.2020.104479
  9. Moore EC, Tolley CL, Bates DW, Slight SP. A systematic review of the impact of health information technology on nurses’ time. J Am Med Informatics Assoc. 2020;27(5):798–807.
  10. Akbar, Muhammad Ikhsan, et al. "Assessing the service quality at health service facilities during the COVID-19 pandemic in North Buton District, Indonesia." Public Health of Indonesia 8.4 (2022): 116-122.
  11. Devi M, Annamalai MAR, Veeramuthu SP. Literature education and industrial revolution 4.0. Univers J Educ Res. 2020;8(3):1027–36.
  12. Alcantara MF, Cao Y, Liu C, Liu B, Brunette M, Zhang N, et al. Improving tuberculosis diagnostics using deep learning and mobile health technologies among resource-poor communities in Perú. Smart Heal. 2017;1–2:66–76.
  13. Hwa SKT, Hijazi MHA, Bade A, Yaakob R, Jeffree MS. Ensemble deep learning for tuberculosis detection using chest X-ray and canny edge detected images. IAES Int J Artif Intell. 2019;8(4):429–35.
  14. Caren GJ, Iskandar D, Pitaloka DAE, Abdulah R, Suwantika AA. COVID-19 Pandemic Disruption on the Management of Tuberculosis Treatment in Indonesia. J Multidiscip Healthc. 2022;15:175–83.
  15. Namatēvs I. Deep Convolutional Neural Networks: Structure, Feature Extraction and Training. Inf Technol Manag Sci. 2018;20(1):40–7.
  16. Meraj SS, Yaakob R, Azman A, Rum SNM, Nazri ASA. Artificial Intelligence in diagnosing tuberculosis: A review. Int J Adv Sci Eng Inf Technol. 2019;9(1):81–91.
  17. Schwalbe N, Wahl B. Artificial intelligence and the future of global health. Lancet [Internet]. 2020;395(10236):1579–86. Available from: http://dx.doi.org/10.1016/S0140-6736(20)30226-9
  18. Nugrahaeni DK, Rosmalaningrum L. Risk Factors in Pulmonary Tuberculosis Treatment Failure. Indones J Public Heal. 2021;16(1):12.
  19. Puspita T, Suryatma A, Simarmata OS, Veridona G, Lestary H, Athena A, et al. Spatial variation of tuberculosis risk in Indonesia 2010-2019. Heal Sci J Indones. 2021;12(2):104–10.
  20. Ministry of Health of Indonesia. Regulation of the Minister of Health of the Republic of Indonesia No. 26 of 2019 concerning Implementing Regulations of Law No. 38 of 2014 concerning Nursing [Internet]. Ministry of Health 2019 p. 1–14.
  21. World Health Organization. End TB by 2030. World Health Organization Reg Off Africa [Internet]. 2017;1–28. Available from: http://www.afro.who.int/publications/framework-implementing-end-tb-strategy-african-region-2016-2020
  22. Bakri S&. Potential side effects of medicine on patients with tuberculosis fixed-dose combination in dr. Pirngadi Hospital, Medan. J Nat. 2020;20(1):10–4.
  23. Mahardani PN, Wati DK, Siloam A, Savitri NPA, Manggala AK. Effectiveness and Safety of Short-term Regimen for Multidrug-resistant Tuberculosis Treatment: A Systematic Review of Cohort Studies. Oman Med J. 2022;37(1).
  24. Fatmawati U, Kusmiati T. Characteristics and the Side Effects of New MDR-TB Treatment. J Respirasi. 2017;3(3):67–73.
  25. Widyasrini ER, Probandari, Ari N. R. Factors Affecting the Success of Multi Drug Resistance (Mdr-Tb) Tuberculosis Treatment in Residential Surakarta. 2017;88.
  26. Hutapea H. Description of Mutation Cases Related to Antiretroviral Resistance in people with HIV-AIDS (PLWHA) in Three Regencies/Cities in Papua Province. Bul Penelit Kesehat. 2018;46(3):199–206.
  27. Vernon A, Fielding K, Savic R, Dodd L, Nahid P. The importance of adherence in tuberculosis treatment clinical trials and its relevance in explanatory and pragmatic trials. PLoS Med. 2019;16(12):e1002884.
  28. Casela M, Cerqueira SMA, Casela T de O, Pereira MA, Dos Santos SQ, Del Pozo FA, et al. Rapid molecular test for tuberculosis: Impact of its routine use at a referral hospital. J Bras Pneumol. 2018;44(2):112–7.
  29. Fitriya L, Artanti KD. Treatment Outcomes of Multidrug Resistant Tuberculosis Patients in East Java From 2014 To 2017. J Berk Epidemiol. 2020;8(2):141.
  30. Herman B, Sirichokchatchawan W, Pongpanich S, Nantasenamat C. Development and performance of CUHASROBUST application for pulmonary rifampicin-resistance tuberculosis screening in Indonesia. PLoS One [Internet]. 2021;16(3 March):1–19. Available from: http://dx.doi.org/10.1371/journal.pone.0249243
  31. Drain PK, Bajema KL, Dowdy D, Dheda K, Naidoo K, Schumacher SG, et al. Incipient and subclinical tuberculosis: A clinical review of early stages and progression of infection. Clin Microbiol Rev. 2018;31(4).
  32. Sulistyono RE, Susanto T, Tristiana RD. Barriers in Tuberculosis Treatment in Rural Areas ( Tengger , Osing and Pandalungan ) in Indonesia Based on Public Health Center Professional Workers Perspectives : a Qualitative Research. 2019;14(1).
  33. Ula M. Application of Machine Learning Clustering K-Means and Linear Regression in determination of the Risk Level of Pulmonary tuberculosis. Indones J Comput Sci. 2023;6(1):336–48.
  34. Li D, Hu L, Peng X, Xiao N, Zhao H, Liu G, et al. A proposed artificial intelligence workflow to address application challenges leveraged on algorithm uncertainty. iScience [Internet]. 2022;25(3):103961. Available from: https://doi.org/10.1016/j.isci.2022.103961
  35. Alqudaihi KS, Aslam N, Khan IU, Almuhaideb AM, Alsunaidi SJ, Ibrahim NMAR, et al. Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques: Challenges and Opportunities. IEEE Access. 2021;9:102327–44.
  36. Heni B. COVID-19, Bacille Calmette-Guerin (BCG) and tuberculosis: Cases and recovery previsions with deep learning sequence prediction. Ing des Syst d’Information. 2020;25(2):165–72.
  37. Nafisah SI, Muhammad G. Tuberculosis detection in chest radiograph using convolutional neural network architecture and explainable artificial intelligence. Neural Comput Appl [Internet]. 2022;6. Available from: https://doi.org/10.1007/s00521-022-07258-6
  38. Indra Z. Development Of An Expert System To Diagnose Tb In Pandemi Time Using The Forward Chaining Method In Puskesmas Medan, Johor. Karismatika. 2022;2(1):40–6.
  39. Muljo HH, Perbangsa AS, Cenggoro TW, Purwandari K, Sudigyo D, Pardamean B. Database System for Storing Tuberculosis Sputum Sample Images as an AI Training Dataset. Int J online Biomed Eng. 2022;18(15):109–21.
  40. Nurdiansyah VV, Cholissodin I, Adikara PP. Classification of Tuberculosis (TB) using the Extreme Learning Machine (ELM) Method. J Pengemb Teknol Inf dan Ilmu Komput [Internet]. 2020;4(5):1387–93. Available from: https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/7237
  41. Apriliani IM, Purba NP, Dewanti LP, Herawati H, Faizal I. The Digital Health Utilization to Improve Successful Treatment of Tuberculosis Patient in Developing Countries : Literature Review. MPPKI Indones Jorunal Heal Promot. 2021;2(1):56–61.
  42. Alfianto T, Anu B. Application of Early Diagnosis of Tuberculosis Using the Certainty Factor Method. Aiti. 2018;15(2):121–7.
  43. Bahri S, Wajhillah R, Adiwisastra MF. Diagnosis of Pulmonary Tuberculosis Based on X-ray Image Using Convolutional Neural Network. Indones J Comput Inf Technol [Internet]. 2021;6(2):181–6. Available from: http://ejournal.bsi.ac.id/ejurnal/index.php/ijcit
  44. Simi Margarat G, Hemalatha G, Mishra A, Shaheen H, Maheswari K, Tamijeselvan S, et al. Early Diagnosis of Tuberculosis Using Deep Learning Approach for IOT Based Healthcare Applications. Comput Intell Neurosci. 2022;2022.
  45. Pradani SA, Kundarto W. Evaluation of Drug Accuracy and Dosage of Anti Tuberculosis Drugs in Pediatric Patients In the Outpatient Installation of RSUDDr. Moewardi Surakarta Period 2016-2017. JPSCR J Pharm Sci Clin Res. 2018;3(2):93.
  46. Weiner J, Maertzdorf J, Sutherland JS, Duffy FJ, Thompson E, Suliman S, et al. Metabolite changes in blood predict the onset of tuberculosis. Nat Commun. 2018;9(1):1–12.
  47. Maja TF, Maposa D. An Investigation of Risk Factors Associated with Tuberculosis Transmission in South Africa Using Logistic Regression Model. Infect Dis Rep. 2022;14(4):609–20.
  48. Herman B, Sirichokchatchawan W, Nantasenamat C, Pongpanich S. Artificial intelligence in overcoming rifampicin resistant-screening challenges in Indonesia: a qualitative study on the user experience of CUHAS-ROBUST. J Heal Res. 2022;36(6):1018–27.
  49. Asrianto, Fachruddin, Indra Taufik Sahli. Penyakit Tuberkulosis di Puskesmas Dosay Sentani Barat Kabupaten Jayapura Tahun 2017-2019. Arter J Ilmu Kesehat. 2020;1(4):333–9.
  50. Priestnall SL, Okumbe N, Orengo L, Okoth R, Gupta S, Gupta NN, et al. The World Of Education In The Internal Area Of Papua Before And After The Impact Of Covid-19. Endocrine [Internet]. 2020;9(May):6. Available from: https://www.slideshare.net/maryamkazemi3/stability-of-colloids%0Ahttps://barnard.edu/sites/default/files/inline/student_user_guide_for_spss.pdf%0Ahttp://www.ibm.com/support%0Ahttp://www.spss.com/sites/dm-book/legacy/ProgDataMgmt_SPSS17.pdf%0Ahttps://www.n
  51. El-Rashidy N, El-Sappagh S, Riazul Islam SM, El-Bakry HM, Abdelrazek S. Mobile health in remote patient monitoring for chronic diseases: Principles, trends, and challenges. Diagnostics. 2021;11(4):1–32.
  52. Sukatemin. Nursing framework for tuberculosis patients with comorbidities in remote areas : a cross-sectional study. Sci Midwifery. 2022;10(5):1–9.
  53. Tukayo I, Jurun H, Hardy S, Saljan M, Swastika IK. The Challenges in Poltekkes Kemenkes Jayapura ( A Case Study ). 2021;71–7.