Modern methods of diagnosis of gynecological diseases

Polina V. Mitrofanova^1*, Khadidja S. Ramazanova¹, Milena B. Beshkok², Aslizhan Z. Goroeva³, Polina O. Sidorenko⁴, Madina E. Khodova¹, Kristina I. Gagloeva¹, Aida T. Palchaeva¹, Vera S. Zhukova¹, Anastasia P. Merkulova¹

¹ Pirogov Russian National Research Medical University, Russia, 117997, Moscow, Ostrovitianov str. 1

² Maikop State Technological University, Russia, 385000, Maykop, Pervomaiskaya str. 191

³ North Ossetian State Medical Academy, Russia, 362019 Vladikavkaz, Pushkinskaya str. 40

⁴ Sechenov First Moscow State Medical University, Russia , 119991 Moscow, Trubetskaya str. 8/2

* Corresponding author

[email protected]

Abstract

The article discusses modern methods of diagnosis of gynecological diseases. In recent years, there has been significant progress in the application of artificial intelligence (AI) in medicine, including gynecology. This paper presents an overview of modern methods of diagnosing gynecological diseases using AI. The authors consider various approaches, such as machine learning and deep learning, and describe their advantages in the context of improving diagnostic accuracy and speed. Special attention is paid to the analysis of large volumes of medical data, which allow us to create more effective diagnostic algorithms. Additionally, the potential of integrating AI into clinical practice and its impact on improving the quality of medical care for women is being considered, opening up new prospects in the field of gynecology.

Imprint

Polina V. Mitrofanova, Khadidja S. Ramazanova, Milena B. Beshkok, Aslizhan Z. Goroeva, Polina O. Sidorenko, Madina E. Khodova, Kristina I. Gagloeva, Aida T. Palchaeva, Vera S. Zhukova, Anastasia P. Merkulova. Modern methods of diagnosis of gynecological diseases. Cardiometry; Issue 31; May 2024; p.138-144; DOI: 10.18137/cardiometry.2024.31.138144; Available from: https://www.cardiometry.net/issues/no31-may-2024/modern-methods-diagnosis

Keywords

Gynecological diseases, Artificial intelligence, Machine learning, Diagnostic algorithms

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