OJOG  Vol.11 No.4 , April 2021
Utilization of Artificial Intelligence for Diagnosis and Management of Urinary Incontinence in Women Residing in Areas with Low Resources: An Overview
Abstract: Urinary incontinence (UI) is a distressing condition involving involuntary loss of urine from the body. Urinary incontinence can negatively impact a persons overall quality of life and lead them into stages of embarrassment and depression. It is an underrepresented and undertreated condition prevalent in women, especially in low socioeconomic regions where women may not be able to express their concerns due to unawareness of diagnosis and treatment/management options. There are different diagnostic and management protocols for UI; however, utilizing artificially intelligent systems is not standard care. This paper overviews the use of artificial intelligence in womens health and as a means of cost-effectively diagnosing patients, and as an avenue for providing low-cost treatments to women that suffer from urinary incontinence in low-resource communities. Studies found that these systems, mainly utilizing artificial neural networks (ANNs) and convolutional neural networks (CNNs), served to be an effective method in diagnosing patients and providing an avenue for personalized treatment for improved patient outcomes. A simple artificial intelligence (AI) model utilizing Multilayer Perceptron (MLP) Networks was proposed to diagnose and manage urinary incontinence.
Cite this paper: Qureshi, A. , Mathur, A. , Alshiek, J. , Shobeiri, S. and Wei, Q. (2021) Utilization of Artificial Intelligence for Diagnosis and Management of Urinary Incontinence in Women Residing in Areas with Low Resources: An Overview. Open Journal of Obstetrics and Gynecology, 11, 403-418. doi: 10.4236/ojog.2021.114040.

[1]   Global Forum on Incontinence (2018) About Incontinence.

[2]   Aoki, Y., Brown, H.W., Brubaker, L., Cornu, J.N., Daly, J.O. and Cartwright, R. (2017) Urinary Incontinence in Women. Nature Reviews Disease Primers, 3, Article ID: 17042.

[3]   Haylen, B.T., de Ridder, D., Freeman, R.M., Swift, S.E., Berghmans, B., Lee, J., et al. (2010) An International Urogynecological Association (IUGA)/International Continence Society (ICS) Joint Report on the Terminology for Female Pelvic Floor Dysfunction. International Urogynecology Journal, 21, 5-26.

[4]   Steers, W.D. (2002) Pathophysiology of Overactive Bladder and Urge Urinary Incontinence. Reviews in Urology, 4, S7-S18.

[5]   Biswas, B., Bhattacharyya, A., Dasgupta, A., Karmakar, A., Mallick, N. and Sembiah, S. (2017) Urinary Incontinence, Its Risk Factors, and Quality of Life: A Study among Women Aged 50 Years and above in a Rural Health Facility of West Bengal. Journal of Mid-Life Health, 8, 130-136.

[6]   Bai, S.W., Jeon, M.J., Kim, J.Y., Chung, K.A., Kim, S.K. and Park, K.H. (2002) Relationship between Stress Urinary Incontinence and Pelvic Organ Prolapsed. International Urogynecology Journal, 13, 256-260.

[7]   Norton, P. and Brubaker, L. (2006) Urinary Incontinence in Women. The Lancet, 367, 57-56.

[8]   Taweel, W.A. and Seyam, R. (2015) Neurogenic Bladder in Spinal Cord Injury Patients. Research and Reports in Urology, 7, 85-99.

[9]   Heydari, F., Motaghed, Z. and Abbaszadeh, S. (2017) Relationship between Hysterectomy and Severity of Female Stress Urinary Incontinence. Electron Physician, 9, 4678-4682.

[10]   Nitti, V.W. (2001) The Prevalence of Urinary Incontinence. Reviews in Urology, 3, S2-S6.

[11]   Khandelwal, C. and Kistler, C. (2013) Diagnosis of Urinary Incontinence. American Family Physician, 87, 543-550.

[12]   Effective Health Care Program (2010) Diagnosis and Comparative Effectiveness of Treatments for Urinary Incontinence in Adult Women.

[13]   Gross, K.B. (2020) How to Strengthen Your Muscles to Eliminate Incontinence.

[14]   Chughtai, B., Kavaler, E., Lee, R., Te, A., Kaplan, S.A. and Lowe, F. (2013) Use of Herbal Supplements for Overactive Bladder. Reviews in Urology, 15, 93-96.

[15]   Nishimura, M., Ohkawara, T., Sato, H., Takeda, H. and Nishihira, J. (2014) Pumpkin Seed Oil Extracted from Cucurbita maxima Improves Urinary Disorder in Human Overactive Bladder. Journal of Traditional and Complementary Medicine, 4, 72-74.

[16]   West, D.M. (2018) What Is Artificial Intelligence? Brookings.

[17]   Tyagi, N. (2020) 6 Major Branches of Artificial Intelligence (AI).

[18]   Wahl, B., Cossy-Gantner, A., Germann, S. and Schwalbe, N.R. (2018) Artificial Intelligence (AI) and Global Health: How Can AI Contribute to Health in Resource-Poor Settings? BMJ Glob Health, 3, e000798.

[19]   Davenport, T. and Kalakota, R. (2019) The Potential for Artificial Intelligence in Healthcare. Future Healthcare Journal, 6, 94-98.

[20]   Sinsky, C., Colligan, L., Li, L., Prgomet, M., Reynolds, S., Goeders, L., et al. (2016) Allocation of Physician Time in Ambulatory Practice: A Time and Motion Study in 4 Specialties. Annals of Internal Medicine, 165, 753-760.

[21]   Amisha, Malik, P., Pathania, M. and Rathaur, V.K. (2019) Overview of Artificial Intelligence in Medicine. Journal of Family Medicine and Primary Care, 8, 2328-2331.

[22]   Iftikhar, P., Kuijpers, M.V., Khayyat, A., Iftikhar, A. and Sa, M.D.D. (2020) Artificial Intelligence: A New Paradigm in Obstetrics and Gynecology Research and Clinical Practice. Cureus, 12, e7134.

[23]   Blix, E., Maude, R., Hals, E., Kisa, S., Karlsen, E., Nohr, E.A., et al. (2019) Intermittent Auscultation Fetal Monitoring during Labour: A Systematic Scoping Review to Identify Methods, Effects, and Accuracy. PLoS ONE, 14, e0219573.

[24]   Guijarro-Berdiñas, B. and Alonso-Betanzos, A. (2002) Empirical Evaluation of a Hybrid Intelligent Monitoring System Using Different Measures of Effectiveness. Artificial Intelligence in Medicine, 24, 71-96.

[25]   Zhao, Z., Deng, Y., Zhang, Y., Zhang, Y., Zhang, X. and Shao, L. (2019) DeepFHR: Intelligent Prediction of Fetal Acidemia Using Fetal Heart Rate Signals Based on Convolutional Neural Network. BMC Medical Informatics and Decision Making, 19, Article No. 286.

[26]   United States Preventive Services Taskforce (2014) Final Recommendation Statement: Gestational Diabetes Mellitus, Screening.

[27]   Polak, S. and Mendyk, A. (2004) Artificial Intelligence Technology as a Tool for Initial GDM Screening. Expert Systems with Applications, 26, 455-460.

[28]   Shen, J., Chen, J., Zheng, Z., Zheng, J., Liu, Z., Song, J., et al. (2020) An Innovative Artificial Intelligence-Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study. Journal of Medical Internet Research, 22, e21573.

[29]   Siristatidis, C., Pouliakis, A., Chrelias, C. and Kassanos, D. (2011) Artificial Intelligence in IVF: A Need. Systems Biology in Reproductive Medicine, 57, 179-185.

[30]   Guh, R.-S., Wu, T.-C.J. and Weng, S.-P. (2011) Integrating Genetic Algorithm and Decision Tree Learning for Assistance in Predicting in Vitro Fertilization Outcomes. Expert Systems with Applications, 38, 4437-4449.

[31]   Orach, C.G. (2009) Health Equity: Challenges in Low Income Countries. African Health Sciences, 9, S49-S51.

[32]   DW.COM. (2019) Access to Health Care a Distant Dream for Most Indian Women.

[33]   Hoodbhoy, Z., Hasan, B. and Siddiqui, K. (2019) Does Artificial Intelligence Have Any Role in Healthcare in Low Resource Settings? Journal of Medical Artificial Intelligence, 2, Article No. 13.

[34]   García del Salto, L., de Miguel Criado, J., del Hoyo, L.F.A., Velasco, L.G., Rivas, P.F., Paradela, M.M., et al. (2014) MR Imaging-Based Assessment of the Female Pelvic Floor. RadioGraphics, 34, 1417-1439.

[35]   Wei, Q., Sikdar, S., Chitnis, P., Rostaminia, G. and Abbas Shobeiri, S. (2017) Patient-Specific Studies of Pelvic Floor Biomechanics Using Imaging. In: Shobeiri, S.A., Ed., Practical Pelvic Floor Ultrasonography, Springer International Publishing, Cham, 337-344.

[36]   Shobeiri, S.A. and Manonai, J.B. (2014) Emerging Imaging Technologies and Techniques. In: Shobeiri, S.A., Ed., Practical Pelvic Floor Ultrasonography, Springer International Publishing, New York, 195-210.

[37]   Kim, K., Egorov, V. and Abbas Shobeiri, S. (2017) Emerging Imaging Technologies and Techniques. In: Shobeiri, S.A., Ed., Practical Pelvic Floor Ultrasonography, Springer International Publishing, Cham, 327-336.

[38]   Ho, D., Quake, S.R., McCabe, E.R.B., Chng, W.J., Chow, E.K., Ding, X., et al. (2020) Enabling Technologies for Personalized and Precision Medicine. Trends in Biotechnology, 38, 497-518.

[39]   Burton, R.J., Albur, M., Eberl, M. and Cuff, S.M. (2019) Using Artificial Intelligence to Reduce Diagnostic Workload without Compromising Detection of Urinary Tract Infections. BMC Medical Informatics and Decision Making, 19, Article No. 171.

[40]   Barzegari, M., Vahidi, B., Safarinejad, M.R. and Hashemipour, M. (2018) Pathological Analysis of Stress Urinary Incontinence in Females Using Artificial Neural Networks.

[41]   Geramipour, A., Makki, S. and Erfanian, A. (2015) Neural Network Based Forward Prediction of Bladder Pressure Using Pudendal Nerve Electrical Activity. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, August 2015, 4745-4748.

[42]   Laurikkala, J., Juhola, M., Lammi, S. and Viikki, K. (1999) Comparison of Genetic Algorithms and Other Classification Methods in the Diagnosis of Female Urinary Incontinence. Methods of Information in Medicine, 38, 125-131.

[43]   Lopes, M.H., Marin, H., Ortega, N.R.S. and Massad, E. (2009) The Use of Expert Systems on the Differential Diagnosis of Urinary Incontinence. Revista da Escola de Enfermagem da USP, 43, 704-710.

[44]   Hung, A.J., Chen, J., Ghodoussipour, S., Oh, P.J., Liu, Z., Nguyen, J., et al. (2019) Deep Learning On automated Performance Metrics and Clinical Features to Predict Urinary Continence Recovery after Robot-Assisted Radical Prostatectomy. BJU International, 124, 487-495.

[45]   Karam, R., Bourbeau, D., Majerus, S., Makovey, I., Goldman, H.B., Damaser, M.S., et al. (2016) Real-Time Classification of Bladder Events for Effective Diagnosis and Treatment of Urinary Incontinence. IEEE Transactions on Biomedical Engineering, 63, 721-729.

[46]   Huang, Y.-L. and Chen, H.-Y. (2007) Computer-Aided Diagnosis of Urodynamic Stress incontinence with Vector-Based Perineal Ultrasound Using Neural Networks. Ultrasound in Obstetrics & Gynecology, 30, 1002-1006.

[47]   Guo, J. and Li, B. (2018) The Application of Medical Artificial Intelligence Technology in Rural Areas of Developing Countries. Health Equity, 2, 174-181.

[48]   Raney, J., Hong, C., Cioban, M., Yasuda, H. and Mo, C. (2020) Technology-Novel, Customizable Pessary for Pelvic Organ Prolapse Treatment. University of Pennsylvania—Penn Center for Innovation.

[49]   Medtronic (2020) Neuromodulation—Urinary and Bowel Incontinence.