Is Artificial Intelligence a New Diagnostic Approach for Patients with Endometriosis?

Artificial Intelligence, Endometriosis, Laparoscopy Dear Editor, Nowadays, artificial intelligence (AI) is increasingly used in the medical industry , primarily machine learning (ML) as one element of AI (1). Specifically , AI has been sug- gested in the gynecological field, especially imaging and ultrasound, to assess the uterus (2) and classify ovarian cysts. As far as we know , no studies have analyzed the pos- sible function of AI in diagnosing endometriosis. Artifi- cial intelligence technologies are promising in medicine to help physicians with diagnostic and therapeutic prob- lems. The introduction of AI in midwifery and gynecology has shown great potential. Artificial intelligence for in- terpreting cardiotocography (CTG) has increased its sen- sitivity from 60% to 94%, with a specificity of 91% (1). In breast imaging, AI is part of everyday clinical practice. Arti- ficial intelligence showed a 5.7% decrease in false-positive

and a 4.9% decrease in false-negative results in mammography screening interpretation, according to US- licensed radiologists participating in the McKinney et al.’s study (2). Hence, it is inevitable that AI will lead to non- invasive diagnoses in women, especially endometriosis (2, 3). The gold standard for diagnosing endometriosis is minimally invasive laparoscopy . This procedure is often difficult, costly , and time-consuming for even the most experienced laparoscopic surgeons, as the patient’s con- dition and characteristics may prevent accurate assess- ment of anatomical structures. Laparoscopic surgery can cause dangerous complications and health problems for women. The introduction of AI can be a significant step in diagnosing endometriosis non-invasively (4). Future AI research in diagnosing endometriosis will likely focus on improving machine learning algorithms. This will be achieved through managing datasets, properly using convolutional neural networks and repetitive neu- ral networks, and integrating several models into one end- to-end model. Finally , validating machine learning algo- rithms with large heterogeneous datasets ensures the gen- eralization of results to different populations. Artificial in- telligence seems to be the first step in the diagnosis of en- dometriosis. Inevitably , the use of artificial intelligence in the diagnosis of endometriosis raises essential questions. Does data interpretation require human supervision, or can we rely on AI? Who is responsible for misdiagnosis? Does relying on automation provided by AI destroy tech- nical skills and experience? Does AI reduce the workload of medical professionals or replace them altogether? The application dimensions of AI in health care are only recently becoming apparent, and the scientific com- munity is only beginning to address many of the critical issues that arise. In the study of Guerriero et al., 106 women with a final diagnosis of rectosigmoid endometriosis were studied using AI (3). Regarding diagnostic accuracy , the neural network model was the best (accuracy 0.73, sensitiv- ity 0.72, specificity 0.73, positive predictive value 0.52, and negative predictive value 0.86). The accuracy of ultrasound markers in suspecting rectosigmoid endometriosis using AI models had similar results to the logistic model (3). The sensitivity and specificity of AI in the study of Bendifallah et al. for the diagnosis of endometriosis ranged from 0.82 to 1.0 and 0.91 to 0.95, respectively (5). Data from a few studies worldwide show that AI can be a good screening test for clinicians (6, 7). The introduc- tion of ML in these settings represents a significant change in medical evaluation because AI can replace diagnostic la- paroscopy . Copyright © 2022, Interventional Pain Medicine and Neuromodulation. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited. Nouri B and Roshandel S Footnotes Authors’ Contribution: B. N. wrote the manuscript in consultation with S. R. S. R. helped to draft the manuscript. Conflict of Interests: The authors certify that they have no affiliations with or involvement in any organization or entity with any financial or non-financial interest in the subject matter or materials discussed in the manuscript. Funding/Support: The author(s) received no financial support for the research, authorship, and/or publication of this article.

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