AUTOMATION OF POLYCYSTIC OVARY SYNDROME DIAGNOSTICS THROUGH MACHINE LEARNING ALGORITHMS IN ULTRASOUND IMAGING
Roman GALAGAN
r.galagan@kpi.uaDepartment of Automation and Non-Destructive Testing Systems “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine (Ukraine)
https://orcid.org/0000-0001-7470-8392
Serhiy ANDREIEV
(Ukraine)
https://orcid.org/0009-0007-4314-6017
Nataliia STELMAKH
National Technical University of Ukraine „Kyiv Polytechnic Institute” (Ukraine)
https://orcid.org/0000-0003-1876-2794
Yaroslava RAFALSKA
Bogomolets National Medical University, Pharmaceutical Faculty, Department of Organization and Economy of Pharmacy (Ukraine)
https://orcid.org/0000-0002-1047-3114
Andrii MOMOT
Department of Automation and Non-Destructive Testing Systems, Igor Sikorsky Kyiv Polytechnic Institute, Faculty of Instrumentation Engineering (Ukraine)
https://orcid.org/0000-0001-9092-6699
Abstract
This article presents a study aimed at using machine learning to automate the analysis of ultrasound images in the diagnosis of polycystic ovary syndrome (PCOS). Today, various laboratory and instrumental methods are used to diagnose PCOS, including the analysis of ultrasound images performed by medical professionals. The peculiarity of such analysis is that it requires high qualification of medical professionals and can be subjective. The aim of this work is to develop a software module based on convolutional neural networks (CNN), which will improve the accuracy and objectivity of diagnosing polycystic disease as one of the clinical manifestations of PCOS. By using CNNs, which have proven to be effective in image processing and classification, it becomes possible to automate the analysis process and reduce the influence of the human factor on the diagnosis result. The article describes a machine learning model based on CNN architecture, which was proposed by the authors for analyzing ultrasound images in order to determine polycystic disease. In addition, the article emphasizes the importance of the interpretability of the CNN model. For this purpose, the Gradient-weighted Class Activation Mapping (Grad-CAM) visualization method was used, which allows to identify the image areas that most affect the model's decision and provides clear explanations for each individual prediction.
Keywords:
polycystic ovary syndrome, PCOS, ultrasound imaging, Neural Networks, grad-CAM, Python programmingReferences
Azziz, R. M. D. (2016). Introduction: Determinants of polycystic ovary syndrome. Fertility and Sterility, 106(1), 4-5. https://doi.org/10.1016/j.fertnstert.2016.05.009
DOI: https://doi.org/10.1016/j.fertnstert.2016.05.016
Google Scholar
Bulsara, J., Patel, P., Soni, A., & Acharya, S. (2021). A review: Brief insight into Polycystic Ovarian syndrome. Endocrine and Metabolic Science, 3, 100085. https://doi.org/10.1016/j.endmts.2021.100085
DOI: https://doi.org/10.1016/j.endmts.2021.100085
Google Scholar
Chai, J., Zeng, H., Li, A., & Ngai, E. W. (2021). Deep Learning in computer vision: A critical review of emerging techniques and application scenarios. Machine Learning with Applications, 6, 100134. https://doi.org/10.1016/j.mlwa.2021.100134
DOI: https://doi.org/10.1016/j.mlwa.2021.100134
Google Scholar
Chicco, D., & Jurman, G. (2023). The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification. BioData Mining, 16, 4. https://doi.org/10.1186/s13040-023-00322-4
DOI: https://doi.org/10.1186/s13040-023-00322-4
Google Scholar
Chicco, D., Tötsch, N., & Jurman, G. (2021). The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation. BioData Mining, 14, 13. https://doi.org/10.1186/s13040-021-00244-z
DOI: https://doi.org/10.1186/s13040-021-00244-z
Google Scholar
Choudhari, A., & Korde, A. (2022). PCOS detection using ultrasound images. Kaggle. Retrieved 01.03.2024 from https://www.kaggle.com/datasets/anaghachoudhari/PCOS-detection-using-ultrasound-images
Google Scholar
Christ, J., & Cedars, M. (2023). Current guidelines for diagnosing PCOS. Diagnostics, 13(6), 1113. https://doi.org/10.3390/diagnostics13061113
DOI: https://doi.org/10.3390/diagnostics13061113
Google Scholar
Deswal, R., Narwal, V., Dang, A., & Pundir, C. S. (2020). The prevalence of polycystic ovary syndrome: A brief review. Journal of human reproductive sciences, 13(4), 261-271. https://doi.org/10.4103/jhrs.jhrs_95_18
DOI: https://doi.org/10.4103/jhrs.JHRS_95_18
Google Scholar
Dwivedi, S., Ujjaliya, M. K., & Kaushik, A. (2019) Assessment of the best predictor for diagnosis of polycystic ovarian disease in color Doppler study of ovarian artery. International Journal of Scientific Study, 6(12), 154-162.
Google Scholar
Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., Liu, Y., Topol, E., Dean, J., & Socher, R. (2021). Deep Learning - enabled medical computer vision. npj Digital Medicine, 4, 5. https://doi.org/10.1038/s41746-020-00376-2
DOI: https://doi.org/10.1038/s41746-020-00376-2
Google Scholar
Garad, R. M., & Teede, H. J. (2020). Polycystic ovary syndrome: improving policies, awareness, and clinical care. Current Opinion in Endocrine and Metabolic Research, 12, 112-118. https://doi.org/10.1016/j.coemr.2020.04.007
DOI: https://doi.org/10.1016/j.coemr.2020.04.007
Google Scholar
Gyliene, A., Straksyte, V. & Zaboriene, I. (2022). Value of ultrasonography parameters in diagnosing polycystic ovary syndrome. Open Medicine, 17(1), 1114-1122. https://doi.org/10.1515/med-2022-0505
DOI: https://doi.org/10.1515/med-2022-0505
Google Scholar
Hassaballah, M., & Awad, A. I. (Eds.). (2020). Deep learning in computer vision: principles and applications. CRC Press Taylor & Francis Group.
DOI: https://doi.org/10.1201/9781351003827
Google Scholar
Hoeger, K., Dokras, A., & Piltonen, T. (2021). Update on PCOS: consequences, challenges, and guiding treatment. The Journal of Clinical Endocrinology & Metabolism, 106(3), e1071-e1083. https://doi.org/10.1210/clinem/dgaa839
DOI: https://doi.org/10.1210/clinem/dgaa839
Google Scholar
Karpiński, R., Krakowski, P., Jonak, J., Machrowska, A., Maciejewski, M., & Nogalski, A. (2022). Diagnostics of articular cartilage damage based on generated acoustic signals using ANN - Part II: Patellofemoral joint. Sensors, 22(10), 3765. https://doi.org/10.3390/s22103765
DOI: https://doi.org/10.3390/s22103765
Google Scholar
Kshatri, S. S., & Singh, D. (2023). Convolutional Neural Network in medical image analysis: A review. Archives of Computational Methods in Engineering, 30, 2793-2810. https://doi.org/10.1007/s11831-023-09898-w
DOI: https://doi.org/10.1007/s11831-023-09898-w
Google Scholar
Liu, J., Wu, Q., Hao, Y., Jiao, M., Wang, X., Jiang, S., & Han, L. (2021). Measuring the global disease burden of polycystic ovary syndrome in 194 countries: Global burden of disease study 2017. Human Reproduction, 36(4), 1108-1119. https://doi.org/10.1093/humrep/deaa371
DOI: https://doi.org/10.1093/humrep/deaa371
Google Scholar
Rasquin, L. I., Anastasopoulou, C., & Mayrin, J. V. (2022). Polycystic ovarian disease. StatPearls.
Google Scholar
Rotterdam ESHRE/ASRM-Sponsored PCOS Consensus Workshop Group. (2004). Revised 2003 consensus on diagnostic criteria and long-term health risks related to polycystic ovary syndrome. Fertility and sterility, 81(1), 19-25. https://doi.org/10.1016/j.fertnstert.2003.10.004
DOI: https://doi.org/10.1016/j.fertnstert.2003.10.004
Google Scholar
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2020). Grad-CAM: Visual explanations from deep networks via gradient-based localization. International Journal of Computer Vision, 128, 336-359. https://doi.org/10.1007/s11263-019-01228-7
DOI: https://doi.org/10.1007/s11263-019-01228-7
Google Scholar
Sirmans, S. M., & Pate, K. A. (2013). Epidemiology, diagnosis, and management of polycystic ovary syndrome. Clinical epidemiology, 6, 1-13. https://doi.org/10.2147/CLEP.S37559
DOI: https://doi.org/10.2147/CLEP.S37559
Google Scholar
Teede, H. J., Misso, M. L., Costello, M. F., Dokras, A., Laven, J., Moran, L., Piltonen, T., & Norman, R. J. (2018). Recommendations from the international evidence-based guideline for the assessment and management of polycystic ovary syndrome. Clinical endocrinology, 89(3), 251-268. https://doi.org/10.1111/cen.13795
DOI: https://doi.org/10.1111/cen.13795
Google Scholar
World Health Organization. (2023). Polycystic ovary syndrome. Retrieved 01.03.2024 from: https://www.who.int/news-room/fact-sheets/detail/polycystic-ovary-syndrome
Google Scholar
Yamashita, R., Nishio, M., Do, R. K. G., & Togashi, K. (2018). Convolutional neural networks: an overview and application in radiology. Insights into Imaging, 9, 611-629. https://doi.org/10.1007/s13244-018-0639-9
DOI: https://doi.org/10.1007/s13244-018-0639-9
Google Scholar
Authors
Roman GALAGANr.galagan@kpi.ua
Department of Automation and Non-Destructive Testing Systems “Igor Sikorsky Kyiv Polytechnic Institute”, Kyiv, Ukraine Ukraine
https://orcid.org/0000-0001-7470-8392
Authors
Nataliia STELMAKHNational Technical University of Ukraine „Kyiv Polytechnic Institute” Ukraine
https://orcid.org/0000-0003-1876-2794
Authors
Yaroslava RAFALSKABogomolets National Medical University, Pharmaceutical Faculty, Department of Organization and Economy of Pharmacy Ukraine
https://orcid.org/0000-0002-1047-3114
Authors
Andrii MOMOTDepartment of Automation and Non-Destructive Testing Systems, Igor Sikorsky Kyiv Polytechnic Institute, Faculty of Instrumentation Engineering Ukraine
https://orcid.org/0000-0001-9092-6699
Statistics
Abstract views: 323PDF downloads: 163
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
Similar Articles
- Nataliya SHABLIY, Serhii LUPENKO, Nadiia LUTSYK, Oleh YASNIY, Olha MALYSHEVSKA, KEYSTROKE DYNAMICS ANALYSIS USING MACHINE LEARNING METHODS , Applied Computer Science: Vol. 17 No. 4 (2021)
- Yuriy TRYUS, Nataliya ANTIPOVA, Kateryna ZHURAVEL, Grygoriy ZASPA, INFORMATION TECHNOLOGY OF STOCK INDEXES FORECASTING ON THE BASE OF FUZZY NEURAL NETWORKS , Applied Computer Science: Vol. 13 No. 1 (2017)
- Katarzyna BARAN, APPLICATION OF THERMAL IMAGING CAMERAS FOR SMARTPHONE: SEEK THERMAL COMPACT PRO AND FLIR ONE PRO FOR HUMAN STRESS DETECTION – COMPARISON AND STUDY , Applied Computer Science: Vol. 20 No. 1 (2024)
- Robert KARPIŃSKI, Jakub GAJEWSKI, Jakub SZABELSKI, Dalibor BARTA, APPLICATION OF NEURAL NETWORKS IN PREDICTION OF TENSILE STRENGTH OF ABSORBABLE SUTURES , Applied Computer Science: Vol. 13 No. 4 (2017)
- KK Praneeth Tellakula, Saravana Kumar R, Sanjoy Deb, A SURVEY OF AI IMAGING TECHNIQUES FOR COVID-19 DIAGNOSIS AND PROGNOSIS , Applied Computer Science: Vol. 17 No. 2 (2021)
- Grzegorz KŁOSOWSKI, Tomasz KLEPKA, Agnieszka NOWACKA, NEURAL CONTROLLER FOR THE SELECTION OF RECYCLED COMPONENTS IN POLYMER-GYPSY MORTARS , Applied Computer Science: Vol. 14 No. 2 (2018)
- Lukas BAUER, Leon STÜTZ, Markus KLEY, BLACK BOX EFFICIENCY MODELLING OF AN ELECTRIC DRIVE UNIT UTILIZING METHODS OF MACHINE LEARNING , Applied Computer Science: Vol. 17 No. 4 (2021)
- Monika KULISZ, Aigerim DUISENBEKOVA, Justyna KUJAWSKA, Danira KALDYBAYEVA, Bibigul ISSAYEVA, Piotr LICHOGRAJ, Wojciech CEL, IMPLICATIONS OF NEURAL NETWORK AS A DECISION-MAKING TOOL IN MANAGING KAZAKHSTAN’S AGRICULTURAL ECONOMY , Applied Computer Science: Vol. 19 No. 4 (2023)
- Monika KULISZ, Justyna KUJAWSKA, Zulfiya AUBAKIROVA, Gulnaz ZHAIRBAEVA, Tomasz WAROWNY, PREDICTION OF THE COMPRESSIVE STRENGTH OF ENVIRONMENTALLY FRIENDLY CONCRETE USING ARTIFICIAL NEURAL NETWORK , Applied Computer Science: Vol. 18 No. 4 (2022)
- Michał TOMCZYK, Anna PLICHTA, Mariusz MIKULSKI, APPLICATION OF WAVELET – NEURAL METHOD TO DETECT BACKLASH ZONE IN ELECTROMECHANICAL SYSTEMS GENERATING NOISES , Applied Computer Science: Vol. 15 No. 4 (2019)
You may also start an advanced similarity search for this article.
