Potential Use and Limitation of Artificial Intelligence to Screen Diabetes Mellitus in Clinical Practice: A Literature Review

Aqsha Nur, Defin Yumnanisha, Sydney Tjandra, Adang Bachtiar, Dante Saksono Harbuwono

Abstract


The burden of undiagnosed diabetes mellitus (DM) is substantial, with approximately 240 million individuals globally unaware of their condition, disproportionately affecting low- and middle-income countries (LMICs), including Indonesia. Without screening, DM and its complications will impose significant pressure on healthcare systems. Current clinical practices for screening and diagnosing DM primarily involve blood or laboratory-based testing which possess limitations on access and cost. To address these challenges, researchers have developed risk-scoring tools to identify high-risk populations. However, considering generalizability, artificial intelligence (AI) technologies offer a promising approach, leveraging diverse data sources for improved accuracy. AI models (i.e., machine learning and deep learning) have yielded prediction performances of up to 98% in various diseases. This article underscores the potential of AI-driven approaches in reducing the burden of DM through accurate prediction of undiagnosed diabetes while highlighting the need for continued innovation and collaboration in healthcare delivery.


Keywords


diabetes mellitus; artificial intelligence; screening; diagnosis

References


Ogurtsova K, Guariguata L, Barengo NC, et al. IDF diabetes Atlas: Global estimates of undiagnosed diabetes in adults for 2021. Diabetes Res Clin Pract 2022;183:109118. doi: 10.1016/j.diabres.2021.109118 [published Online First: 20211206]

Magliano DJ, Boyko EJ. The International Diabetes Federation (IDF) Atlas 10th Edition. 10th ed. Brussels: International Diabetes Federation 2021.

Heydari I, Radi V, Razmjou S, Amiri A. Chronic complications of diabetes mellitus in newly diagnosed patients. International Journal of Diabetes Mellitus 2010;2(1):61-3. doi: https://doi.org/10.1016/j.ijdm.2009.08.001

Gopalan A, Mishra P, Alexeeff SE, et al. Prevalence and predictors of delayed clinical diagnosis of Type 2 diabetes: a longitudinal cohort study. Diabet Med 2018;35(12):1655-62. doi: 10.1111/dme.13808 [published Online First: 20180921]

Guariguata L, Whiting D, Weil C, Unwin N. The International Diabetes Federation diabetes atlas methodology for estimating global and national prevalence of diabetes in adults. Diabetes Research and Clinical Practice. 2011;94(3):322-32. doi: https://doi.org/10.1016/j.diabres.2011.10.040

National Health Policy Agency. Basic Health Research 2018 [Website]. Jakarta: Ministry of Health Republic of Indonesia,; 2020 [cited 2024 1 May 2024]. Available from: https://layanandata.kemkes.go.id/katalog-data/riskesdas/ketersediaan-data/riskesdas-2018 accessed 1 May 2024 2024.

Kementerian Kesehatan. Penyakit berbiaya tertinggi dalam program JKN tahun 2022. Jakarta: Kementerian Kesehatan, 2023.

Manne-Goehler J, Geldsetzer P, Agoudavi K, et al. Health system performance for people with diabetes in 28 low- and middle-income countries: A cross-sectional study of nationally representative surveys. PLoS Med. 2019;16(3):e1002751. doi: 10.1371/journal.pmed.1002751 [published Online First: 20190301]

Kementerian Kesehatan. Program tematik diabetes melitus tipe-2 tahun 2022. Jakarta: Kementerian Kesehatan, 2023.

Siswati T, Kasjono HS, Olfah Y. “Posbindu PTM”: The Key of Early Detection and Decreasing Prevalence of Non-Communicable Diseases in Indonesia. Iran J Public Health. 2022;51(7):1683-84. doi: 10.18502/ijph.v51i7.10105

Alkaff FF, Illavi F, Salamah S, et al. The impact of the Indonesian chronic disease management program (PROLANIS) on metabolic control and renal function of type 2 diabetes mellitus patients in primary care setting. J Prim Care Community Health. 2021;12:2150132720984409. doi: 10.1177/2150132720984409

Cho NH, Shaw JE, Karuranga S, et al. IDF diabetes atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res Clin Pract. 2018;138:271-81. doi: 10.1016/j.diabres.2018.02.023 [published Online First: 20180226]

Association AD. 2. Classification and diagnosis of diabetes: standards of medical care in diabetes—2021. Diabetes Care. 2021;44(Suppl1):S15-S33.

American Diabetes Association. Classification and diagnosis of diabetes: Standards of medical care in diabetes-2020. Diabetes Care. 2020;43(Suppl 1):S14-S31. doi: 10.2337/dc20-S002

Gepts W. Pathologic anatomy of the pancreas in juvenile diabetes mellitus. Diabetes. 1965;14(10):619-33. doi: 10.2337/diab.14.10.619

Atkinson MA, Eisenbarth GS, Michels AW. Type 1 diabetes. The lancet. 2014;383(9911):69-82.

DeFronzo RA. From the triumvirate to the ominous octet: A new paradigm for the treatment of type 2 diabetes mellitus. Diabetes. 2009;58(4):773-95. doi: 10.2337/db09-9028

Bellou V, Belbasis L, Tzoulaki I, Evangelou E. Risk factors for type 2 diabetes mellitus: An exposure-wide umbrella review of meta-analyses. PloS One. 2018;13(3):e0194127. doi: 10.1371/journal.pone.0194127 [published Online First: 20180320]

Gilbert R, Logan S, Moyer VA, Elliott EJ. Assessing diagnostic and screening tests: Part 1. Concepts. West J Med 2001;174(6):405-9. doi: 10.1136/ewjm.174.6.405

Perkumpulan Endokrinologi Indonesia. Pedoman pengelolaan dan pencegahan DMT2 dewasa di Indonesia. Jakarta: PERKENI, 2021.

Committee ADAPP. Introduction and methodology: Standards of care in diabetes—2024. Diabetes Care. 2023;47(Suppl.1):S1-S4. doi: 10.2337/dc24-SINT

World Health Organization. Diagnosis and management of type 2 diabetes (HEARTS-D). Geneva: World Health Organization; 2020.

Wee BF, Sivakumar S, Lim KH, et al. Diabetes detection based on machine learning and deep learning approaches. Multimedia Tools and Applications. 2024;83(8):24153-85. doi: 10.1007/s11042-023-16407-5

AshaRani PV, Devi F, Wang P, et al. Factors influencing uptake of diabetes health screening: a mixed methods study in Asian population. BMC Public Health. 2022;22(1):1511. doi: 10.1186/s12889-022-13914-2 [published Online First: 20220809]

Nichols JH. Blood glucose testing in the hospital: error sources and risk management. J Diabetes Sci Technol 2011;5(1):173-7. doi: 10.1177/193229681100500124 [published Online First: 20110101]

El Sayed NA, Aleppo G, Aroda VR, et al. Classification and diagnosis of diabetes: Standards of care in diabetes-2023. Diabetes Care. 2023;46(Suppl 1):S19-S40. doi: 10.2337/dc23-S002

Ohde S, Moriwaki K, Takahashi O. Cost-effectiveness analysis for HbA1c test intervals to screen patients with type 2 diabetes based on risk stratification. BMC Endocr Disord. 2021;21(1):105. doi: 10.1186/s12902-021-00771-0 [published Online First: 20210522]

Heidt B, Siqueira WF, Eersels K, et al. Point of care diagnostics in resource-limited settings: A review of the present and future of PoC in its most needed environment. Biosensors (Basel) 2020;10(10) doi: 10.3390/bios10100133 [published Online First: 20200924]

Gregg EW, Sattar N, Ali MK. The changing face of diabetes complications. Lancet Diabetes Endocrinol. 2016;4(6):537-47. doi: 10.1016/S2213-8587(16)30010-9 [published Online First: 20160504]

Herman WH, Ye W, Griffin SJ, et al. Early detection and treatment of type 2 diabetes reduce cardiovascular morbidity and mortality: a simulation of the results of the anglo-danish-dutch study of intensive treatment in people with screen-detected diabetes in primary care (ADDITION-Europe). Diabetes Care. 2015;38(8):1449-55. doi: 10.2337/dc14-2459 [published Online First: 20150518]

Lindstrom J, Tuomilehto J. The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabetes Care. 2003;26(3):725-31. doi: 10.2337/diacare.26.3.725

Mohan V, Deepa R, Deepa M, et al. A simplified Indian diabetes risk score for screening for undiagnosed diabetic subjects. J Assoc Physicians India. 2005;53:759-63.

McGregor MS, Pinkham C, Ahroni JH, et al. The American Diabetes Association risk test for diabetes. Diabetes Care. 1995;18(4):585-6. doi: 10.2337/diacare.18.4.585b

Sulistiowati E, Pradono J. Development of a validated diabetes risk chart as a simple tool to predict the onset of diabetes in Bogor, Indonesia. J ASEAN Fed Endocr Soc. 2022;37(1):46-52. doi: 10.15605/jafes.037.01.09 [published Online First: 20220427]

Noble D, Mathur R, Dent T, et al. Risk models and scores for type 2 diabetes: systematic review. BMJ. 2011;343:d7163. doi: 10.1136/bmj.d7163

Martinez-Millana A, Argente-Pla M, Valdivieso Martinez B, et al. Driving type 2 diabetes risk scores into clinical practice: Performance analysis in hospital settings. J Clin Med. 2019;8(1) doi: 10.3390/jcm8010107 [published Online First: 20190117]

Wang L, Zhang Y, Wang D, et al. Artificial intelligence for COVID-19: A systematic review. Front Med (Lausanne). 2021;8:704256. doi: 10.3389/fmed.2021.704256 [published Online First: 20210930]

Amisha, Malik P, Pathania M, Rathaur VK. Overview of artificial intelligence in medicine. J Family Med Prim Care. 2019;8(7):2328-31. doi: 10.4103/jfmpc.jfmpc_440_19

Aijaz SF, Khan SJ, Azim F, et al. Deep learning application for effective classification of different types of psoriasis. J Healthc Eng. 2022;2022:7541583. doi: 10.1155/2022/7541583 [published Online First: 20220115]

Chuang CL. Case-based reasoning support for liver disease diagnosis. Artif Intell Med. 2011;53(1):15-23. doi: 10.1016/j.artmed.2011.06.002 [published Online First: 20110714]

Owais M, Arsalan M, Choi J, et al. Artificial intelligence-based classification of multiple gastrointestinal diseases using endoscopy videos for clinical diagnosis. J Clin Med. 2019;8(7) doi: 10.3390/jcm8070986 [published Online First: 20190707]

Yildirim O, Plawiak P, Tan RS, Acharya UR. Arrhythmia detection using deep convolutional neural network with long duration ECG signals. Comput Biol Med. 2018;102:411-20. doi: 10.1016/j.compbiomed.2018.09.009 [published Online First: 20180915]

Kanegae H, Suzuki K, Fukatani K, et al. Highly precise risk prediction model for new-onset hypertension using artificial intelligence techniques. J Clin Hypertens. (Greenwich) 2020;22(3):445-50. doi: 10.1111/jch.13759 [published Online First: 20191209]

Luo H, Xu G, Li C, et al. Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study. Lancet Oncol. 2019;20(12):1645-54. doi: 10.1016/S1470-2045(19)30637-0 [published Online First: 20191004]

Ghaffar Nia N, Kaplanoglu E, Nasab A. Evaluation of artificial intelligence techniques in disease diagnosis and prediction. Discover Artificial Intelligence 2023;3(1):5. doi: 10.1007/s44163-023-00049-5

Al Kuwaiti A, Nazer K, Al-Reedy A, et al. A review of the role of artificial intelligence in healthcare. J Pers Med. 2023;13(6) doi: 10.3390/jpm13060951 [published Online First: 20230605]

Wu X, Liu X, Zhou Y. Proceedings of 2021 chinese intelligent systems conference: review of unsupervised learning techniques in lecture notes in electrical engineering. Singapore: Springer; 2022.

Woldaregay AZ, Arsand E, Walderhaug S, et al. Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes. Artif Intell Med. 2019;98:109-34. doi: 10.1016/j.artmed.2019.07.007 [published Online First: 20190726]

Williams BM, Borroni D, Liu R, et al. An artificial intelligence-based deep learning algorithm for the diagnosis of diabetic neuropathy using corneal confocal microscopy: a development and validation study. Diabetologia. 2020;63(2):419-30. doi: 10.1007/s00125-019-05023-4 [published Online First: 20191112]

Fregoso-Aparicio L, Noguez J, Montesinos L, García-García JA. Machine learning and deep learning predictive models for type 2 diabetes: a systematic review. Diabetol Metab Syndr. 2021;13(1):148. doi: 10.1186/s13098-021-00767-9 [published Online First: 20211220]

Khanna NN, Maindarkar MA, Viswanathan V, et al. Economics of artificial intelligence in healthcare: Diagnosis vs. treatment. Healthcare. 2022;10(12):2493.

Wolff J, Pauling J, Keck A, Baumbach J. The economic impact of artificial intelligence in health care: Systematic review. J Med Internet Res. 2020;22(2):e16866. doi: 10.2196/16866

Ayesha S, Kashif M, Talib R. Overview and comparative study of dimensionality reduction techniques for high dimensional data. Information Fusion. 2020;59 doi: 10.1016/j.inffus.2020.01.005


Full Text: PDF

Refbacks

  • There are currently no refbacks.