Artificial intelligence (AI) in the detection of rectosigmoid deep endometriosis

Guerriero, S.; Pascual, M.; Ajossa, Silvia; Neri, Manuela; Musa, Eleonora; Graupera, B.; Rodríguez, I.; Alcázar, Juan Luis

doi:10.1016/j.ejogrb.2021.04.012

Cited by 22 publications

(16 citation statements)

References 38 publications

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“…SVM also produced the highest SE at 84% while Naïve Bayes and decision tree showed the highest SP (77%). The pooled SE for the models with highest accuracy in each study was 88% while the pooled SP was 89.7% [59][60][61] .…”

Section: Diagnostic or Predictive Models For Endometriosis Using Mixe...mentioning

confidence: 91%

“…Reid et al 60 also produced two logistic regression models using different imaging variables; the accuracy of both models was higher than the logistic regression model produced by Guerriero et al 59 indicating that perhaps the inputs for Reid's model 60 played a role in the higher accuracy, SE and SP. All three studies in this category explored "sliding sign" on transvaginal ultrasound as an important features in their models.…”

Section: Diagnostic or Predictive Models For Endometriosis Using Mixe...mentioning

confidence: 97%

Section: Diagnostic or Predictive Models For Endometriosis Using Mixe...mentioning

confidence: 99%

“…Diagnostic or predictive models for endometriosis using imaging Only three studies [59][60][61] explored the use of imaging variables as their primary inputs for their AI models as seen in Table 7. Guerriero 59 built models specifically for rectosigmoid endometriosis and compared the accuracy of the different AI methods using the same inputs for each model. This specific study allows one to draw conclusions about the accuracy of different methodologies in developing predictive models to increase suspicion for rectosigmoid endometriosis.…”

Section: Diagnostic or Predictive Models For Endometriosis Using Mixe...mentioning

confidence: 99%

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Clinical use of artificial intelligence in endometriosis: a scoping review

et al. 2022

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Endometriosis is a chronic, debilitating, gynecologic condition with a non-specific clinical presentation. Globally, patients can experience diagnostic delays of ~6 to 12 years, which significantly hinders adequate management and places a significant financial burden on patients and the healthcare system. Through artificial intelligence (AI), it is possible to create models that can extract data patterns to act as inputs for developing interventions with predictive and diagnostic accuracies that are superior to conventional methods and current tools used in standards of care. This literature review explored the use of AI methods to address different clinical problems in endometriosis. Approximately 1309 unique records were found across four databases; among those, 36 studies met the inclusion criteria. Studies were eligible if they involved an AI approach or model to explore endometriosis pathology, diagnostics, prediction, or management and if they reported evaluation metrics (sensitivity and specificity) after validating their models. Only articles accessible in English were included in this review. Logistic regression was the most popular machine learning method, followed by decision tree algorithms, random forest, and support vector machines. Approximately 44.4% (n = 16) of the studies analyzed the predictive capabilities of AI approaches in patients with endometriosis, while 47.2% (n = 17) explored diagnostic capabilities, and 8.33% (n = 3) used AI to improve disease understanding. Models were built using different data types, including biomarkers, clinical variables, metabolite spectra, genetic variables, imaging data, mixed methods, and lesion characteristics. Regardless of the AI-based endometriosis application (either diagnostic or predictive), pooled sensitivities ranged from 81.7 to 96.7%, and pooled specificities ranged between 70.7 and 91.6%. Overall, AI models displayed good diagnostic and predictive capacity in detecting endometriosis using simple classification scenarios (i.e., differentiating between cases and controls), showing promising directions for AI in assessing endometriosis in the near future. This timely review highlighted an emerging area of interest in endometriosis and AI. It also provided recommendations for future research in this field to improve the reproducibility of results and comparability between models, and further test the capacity of these models to enhance diagnosis, prediction, and management in endometriosis patients.

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Section: Diagnostic or Predictive Models For Endometriosis Using Mixe...mentioning

confidence: 91%

Section: Diagnostic or Predictive Models For Endometriosis Using Mixe...mentioning

confidence: 97%

Section: Diagnostic or Predictive Models For Endometriosis Using Mixe...mentioning

confidence: 99%

Section: Diagnostic or Predictive Models For Endometriosis Using Mixe...mentioning

confidence: 99%

See 2 more Smart Citations

Clinical use of artificial intelligence in endometriosis: a scoping review

et al. 2022

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“…Artificial intelligence showed a 5.7% decrease in false-positive results and a 4.9% decrease in false-negative results in mammography screening interpretation, according to USlicensed radiologists participating in the McKinney et al's study (2). Hence, it is inevitable that AI will lead to noninvasive diagnoses in women, especially endometriosis (2,3).…”

Section: Dear Editormentioning

confidence: 99%

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

Nouri

Roshandel

2022

Interv Pain Med Neuromod

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Are sonographers the future ‘gold standard’ in the diagnosis of endometriosis?

Deslandes,

Panuccio,

Avery

et al. 2024

Sonography

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Diagnosis of endometriosis has traditionally relied on laparoscopic surgery, which was considered the ‘gold standard’ diagnostic tool. This is not ideal as surgery carries risk, is expensive, is difficult to access, and disrupts patients work or education due to the recovery time needed. As such, imaging has been investigated as a potential method for non‐invasive diagnosis, with transvaginal ultrasound showing high diagnostic accuracy for ovarian endometriomas and deep endometriosis. The advances in imaging capability led to recent international guidelines suggesting laparoscopy is no longer the ‘gold‐standard’ for diagnosis and encouraging clinicians to utilise medical imaging as part of their diagnostic work‐up for endometriosis. Imaging is emerging as not only a tool for planning endometriosis surgery but increasingly as the first approach for initial diagnosis. Given that transvaginal ultrasound is the primary imaging modality for assessment of gynaecological conditions, it is inevitable that sonographers will have a significant future role in endometriosis diagnosis. This moves away from endometriosis diagnosis being the exclusive realm of laparoscopic surgeons and increasingly involves medical imaging specialists. This review article will describe the origins of endometriosis ultrasound and the current capabilities of transvaginal ultrasound in this field. The expectations of sonographers in this evolving space will be explored, as well as recent novel research findings to gain insight into what the future of endometriosis diagnosis with ultrasound may look like.

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Artificial intelligence (AI) in the detection of rectosigmoid deep endometriosis

Cited by 22 publications

References 38 publications

Clinical use of artificial intelligence in endometriosis: a scoping review

Clinical use of artificial intelligence in endometriosis: a scoping review

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

Are sonographers the future ‘gold standard’ in the diagnosis of endometriosis?

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