Application of Artificial Intelligence for Diagnosing Tumors in the Female Reproductive System: A Systematic Review

Authors

DOI:

https://doi.org/10.62486/agmu202554

Keywords:

Tumors, AI, Reproductive System, CNN

Abstract

The diagnosis of tumors in the female reproductive system is crucial for effective treatment and patient outcomes. The advent of artificial intelligence (AI) has introduced new possibilities for enhancing diagnostic accuracy and efficiency. A comprehensive search across PubMed, Scopus, and Web of Science for articles published from 2018 to 2023 on artificial intelligence (AI), machine learning (ML), deep learning (DL), and convolutional neural networks (CNN) in diagnosing cancers of the female reproductive system yielded 15,900 articles. After a rigorous screening process excluding conference proceedings, book chapters, reports, non-English publications, and duplicates, 98 unique peer-reviewed journal articles remained. These were further assessed for relevance and quality, resulting in the final inclusion of 29 high-quality articles. The review includes a summary of various AI methodologies used, their diagnostic accuracy, and comparative performance against traditional diagnostic methods. The findings indicate a significant improvement in diagnostic precision and efficiency when AI is employed. AI holds substantial promise for enhancing the diagnosis of tumors in the female reproductive system. Future research should focus on larger-scale studies and the integration of AI into clinical workflows to fully realize its potential

References

Alzboon MS. Survey on Patient Health Monitoring System Based on Internet of Things. Inf Sci Lett. 2022;11(4):1183–90.

Alzboon MS, Al-Batah M, Alqaraleh M, Abuashour A, Bader AF. A Comparative Study of Machine Learning Techniques for Early Prediction of Prostate Cancer. In: 2023 IEEE 10th International Conference on Communications and Networking, ComNet 2023 - Proceedings. 2023. p. 1–12.

Alzboon M, Alomari SA, Al-Batah MS, Banikhalaf M. The characteristics of the green internet of things and big data in building safer, smarter, and sustainable cities. Int J Eng & Technol. 2017;6(3):83–92.

Alzboon MS, Al-Batah M, Alqaraleh M, Abuashour A, Bader AF. A Comparative Study of Machine Learning Techniques for Early Prediction of Diabetes. In: 2023 IEEE 10th International Conference on Communications and Networking, ComNet 2023 - Proceedings. 2023. p. 1–12.

Al-shanableh N, Alzyoud M, Al-husban RY, Alshanableh NM, Al-Oun A, Al-Batah MS, et al. Advanced Ensemble Machine Learning Techniques for Optimizing Diabetes Mellitus Prognostication: A Detailed Examination of Hospital Data. Data Metadata. 2024;3:363.

Al Tal S, Al Salaimeh S, Ali Alomari S, Alqaraleh M. The modern hosting computing systems for small and medium businesses. Acad Entrep J. 2019;25(4):1–7.

Alzboon MS, Bader AF, Abuashour A, Alqaraleh MK, Zaqaibeh B, Al-Batah M. The Two Sides of AI in Cybersecurity: Opportunities and Challenges. In: Proceedings of 2023 2nd International Conference on Intelligent Computing and Next Generation Networks, ICNGN 2023. 2023.

Ahmad A, Alzboon MS, Alqaraleh MK. Comparative Study of Classification Mechanisms of Machine Learning on Multiple Data Mining Tool Kits. Am J Biomed Sci Res 2024 [Internet]. 2024;22(1):577–9. Available from: www.biomedgrid.com

Alzboon MS, Al-Batah MS, Alqaraleh M, Abuashour A, Bader AFH. Early Diagnosis of Diabetes: A Comparison of Machine Learning Methods. Int J online Biomed Eng. 2023;19(15):144–65.

Al-Batah MS, Alzboon MS, Alzyoud M, Al-Shanableh N. Enhancing Image Cryptography Performance with Block Left Rotation Operations. Appl Comput Intell Soft Comput. 2024;2024(1):3641927.

Alomari SA, Alqaraleh M, Aljarrah E, Alzboon MS. Toward achieving self-resource discovery in distributed systems based on distributed quadtree. J Theor Appl Inf Technol. 2020;98(20):3088–99.

Al-Batah M, Zaqaibeh B, Alomari SA, Alzboon MS. Gene Microarray Cancer classification using correlation based feature selection algorithm and rules classifiers. Int J online Biomed Eng. 2019;15(8):62–73.

Alqaraleh M, Alzboon MS, Al-Batah MS, Wahed MA, Abuashour A, Alsmadi FH. Harnessing Machine Learning for Quantifying Vesicoureteral Reflux: A Promising Approach for Objective Assessment. Int J Online & Biomed Eng. 2024;20(11).

Al-Batah MS, Alzboon MS, Alazaidah R. Intelligent Heart Disease Prediction System with Applications in Jordanian Hospitals. Int J Adv Comput Sci Appl. 2023;14(9):508–17.

Cho H-W, Cho H, Kim J, Kim S, Lee S, Song JY, et al. Pelvic ultrasound-based deep learning models for accurate diagnosis of ovarian cancer: retrospective multicenter study. American Society of Clinical Oncology; 2024.

Gao X, Li H, You C, Zhou L, Shi Q, Yang Z, et al. Challenges and Advances on Explainable Artificial Intelligence (AI): Diagnosing and Treating Tumors of the Female Reproductive Systems. 2024;

Ma L, Huang L, Chen Y, Zhang L, Nie D, He W, et al. AI diagnostic performance based on multiple imaging modalities for ovarian tumor: A systematic review and meta-analysis. Front Oncol. 2023;13:1133491.

Moro F, Ciancia M, Zace D, Vagni M, Tran HE, Giudice MT, et al. Role of artificial intelligence applied to ultrasound in gynecology oncology: A systematic review. Int J Cancer. 2024.

Al-Batah, M. S., & Al-Eiadeh, M. R. (2024). An improved binary Crow-JAYA optimization system with various evolution operators, such as mutation for finding the max clique in the dense graph. International Journal of Computing Science and Mathematics, 19(4), 327-338. Inderscience Publishers.

Alzboon MS. Internet of things between reality or a wishing-list: a survey. Int J Eng & Technol. 2018;7(2):956–61.

Al-Batah, M. S. (2019). Integrating the principal component analysis with partial decision tree in microarray gene data. IJCSNS International Journal of Computer Science and Network Security, 19(3), 24-29.

Alzboon MS, Qawasmeh S, Alqaraleh M, Abuashour A, Bader AF, Al-Batah M. Machine Learning Classification Algorithms for Accurate Breast Cancer Diagnosis. In: 2023 3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023. 2023.

Alzboon MS, Aljarrah E, Alqaraleh M, Alomari SA. Nodexl Tool for Social Network Analysis. Vol. 12, Turkish Journal of Computer and Mathematics Education. 2021.

Al-Batah, M. S. (2019). Ranked features selection with MSBRG algorithm and rules classifiers for cervical cancer. International Journal of Online and Biomedical Engineering (iJOE), 15(12), 4. https://doi.org/10.3991/ijoe.v15i12.10803

Alzboon MS, Al-Batah MS. Prostate Cancer Detection and Analysis using Advanced Machine Learning. Int J Adv Comput Sci Appl. 2023;14(8):388–96.

Al-Batah, M. S. (2014). Testing the probability of heart disease using classification and regression tree model. Annual Research & Review in Biology, 4(11), 1713–1725. https://doi.org/10.9734/arrb/2014/7786

Alzboon MS, Qawasmeh S, Alqaraleh M, Abuashour A, Bader AF, Al-Batah M. Pushing the Envelope: Investigating the Potential and Limitations of ChatGPT and Artificial Intelligence in Advancing Computer Science Research. In: 2023 3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023. 2023.

Al-Batah, M. S. (2019). Automatic diagnosis system for heart disorder using ECG peak recognition with ranked features selection. International Journal of Circuits, Systems and Signal Processing, 13, 391-398.

Alzboon M. Semantic Text Analysis on Social Networks and Data Processing: Review and Future Directions. Inf Sci Lett. 2022;11(5):1371–84.

Al-Batah, M. S., & Al-Eiadeh, M. R. (2024). An improved discrete Jaya optimization algorithm with mutation operator and opposition-based learning to solve the 0-1 knapsack problem. International Journal of Mathematics in Operational Research, 26(2), 143-169.

Downloads

Published

2025-01-01

How to Cite

1.
Abdel Wahed M, Alqaraleh M, Salem Alzboon M, Subhi Al-Batah M. Application of Artificial Intelligence for Diagnosing Tumors in the Female Reproductive System: A Systematic Review. Multidisciplinar (Montevideo) [Internet]. 2025 Jan. 1 [cited 2024 Oct. 18];3:54. Available from: https://multidisciplinar.ageditor.uy/index.php/multidisciplinar/article/view/54