<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec><h3>Background</h3>
<p>Cervical cancer is the fourth most common female malignancy worldwide. In developing countries, it is the most common subtype of cancer and the third leading cause of cancer mortality among women. Artificial intelligence has the potential to be of real use in the prevention and prompt diagnosis of cervical cancer. The aim of our study was to develop a medical platform consisting of an automated observation sheet containing colposcopy data, a software that would use a machine learning module based on clinical and image data for diagnosis and treatment, and a telemedicine module to enable collaboration between gynecologists.</p>
</sec>
<sec><h3>Materials and methods</h3>
<p>Clinical and colposcopy image data from 136 patients were introduced into a machine learning module designed to generate an algorithm for proposing a preliminary diagnosis and treatment. The clinical and imaging data were corroborated to generate six options: ‘Follow-up’, ‘Pharmacotherapy’, ‘Biopsy’, ‘Curettage’, ‘DTC’, and ‘Conization’.</p>
</sec>
<sec><h3>Results</h3>
<p>Data generated by the machine learning module regarding treatment options were compared with the opinion of gynecologists and yielded an accuracy of 78% for ‘Follow-up’, 81% for ‘Pharmacotherapy’, 84% for ‘Biopsy’, 90% for ‘Curettage’, 96% for ‘DTC’, and 81% for ‘Conization’.</p>
</sec>
<sec><h3>Conclusions</h3>
<p>The developed software can be an important step towards the digitization of existing gynecology offices and the creation of intelligently automated gynecology offices related to prevention and treatment of cervical cancer. More data is needed to improve the accuracy of the developed software.</p>
</sec>
</abstract>

Background
Cervical cancer is the fourth most common female malignancy worldwide. In developing countries, it is the most common subtype of cancer and the third leading cause of cancer mortality among women. Artificial intelligence has the potential to be of real use in the prevention and prompt diagnosis of cervical cancer. The aim of our study was to develop a medical platform consisting of an automated observation sheet containing colposcopy data, a software that would use a machine learning module based on clinical and image data for diagnosis and treatment, and a telemedicine module to enable collaboration between gynecologists.

Materials and methods
Clinical and colposcopy image data from 136 patients were introduced into a machine learning module designed to generate an algorithm for proposing a preliminary diagnosis and treatment. The clinical and imaging data were corroborated to generate six options: ‘Follow-up’, ‘Pharmacotherapy’, ‘Biopsy’, ‘Curettage’, ‘DTC’, and ‘Conization’.

Results
Data generated by the machine learning module regarding treatment options were compared with the opinion of gynecologists and yielded an accuracy of 78% for ‘Follow-up’, 81% for ‘Pharmacotherapy’, 84% for ‘Biopsy’, 90% for ‘Curettage’, 96% for ‘DTC’, and 81% for ‘Conization’.

Conclusions
The developed software can be an important step towards the digitization of existing gynecology offices and the creation of intelligently automated gynecology offices related to prevention and treatment of cervical cancer. More data is needed to improve the accuracy of the developed software.

Digitization of Gynecology Using Artificial Intelligence: Cervical Mapping Corroborated With Clinical Data for Conization Necessity

“Grigore T. Popa” University of Medicine and Pharmacy

Journal of Interdisciplinary Medicine

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Background
Cervical cancer is the fourth most common female malignancy worldwide. In developing countries, it is the most common subtype of cancer and the...

Digitization of Gynecology Using Artificial Intelligence: Cervical Mapping Corroborated With Clinical Data for Conization Necessity

Pubblicato online: 21 set 2023

Pagine: 55 - 59

Ricevuto: 03 set 2023

Accettato: 12 set 2023

DOI: https://doi.org/10.2478/jim-2023-0013

Parole chiave
cervical cancer, colposcopy, human papilloma virus, machine learning, artificial intelligence

© 2023 Dorina Adelina Minciună et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Digitization of Gynecology Using Artificial Intelligence: Cervical Mapping Corroborated With Clinical Data for Conization Necessity

Pubblicato online: 21 set 2023

Pagine: 55 - 59

Ricevuto: 03 set 2023

Accettato: 12 set 2023

DOI: https://doi.org/10.2478/jim-2023-0013

Parole chiavecervical cancer, colposcopy, human papilloma virus, machine learning, artificial intelligence

© 2023 Dorina Adelina Minciună et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
cervical cancer, colposcopy, human papilloma virus, machine learning, artificial intelligence