An Analogy of Endometriosis Recognition Using Machine Learning Techniques

Visalaxi, S.; Punnoose, Dinah; SudalaiMuthu, T

doi:10.1109/icicv50876.2021.9388403

Cited by 13 publications

(7 citation statements)

References 11 publications

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“…With the increasing use of medical imaging, videos, and pathological samples, machine learning and deep learning approaches are playing a growing role in diagnosis [ 61 ]. A machine learning model for endometriosis based on a screening questionnaire was shown to produce an AUC of 0.5–0.9 in the training and validation sets based on the combination of 16 common criteria such as age, pain, and family history [ 62 ].…”

Section: Discussionmentioning

confidence: 99%

Revisiting the Risk Factors for Endometriosis: A Machine Learning Approach

Blass

Sahar

Shraibman

et al. 2022

JPM

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Endometriosis is a condition characterized by implants of endometrial tissues into extrauterine sites, mostly within the pelvic peritoneum. The prevalence of endometriosis is under-diagnosed and is estimated to account for 5–10% of all women of reproductive age. The goal of this study was to develop a model for endometriosis based on the UK-biobank (UKB) and re-assess the contribution of known risk factors to endometriosis. We partitioned the data into those diagnosed with endometriosis (5924; ICD-10: N80) and a control group (142,723). We included over 1000 variables from the UKB covering personal information about female health, lifestyle, self-reported data, genetic variants, and medical history prior to endometriosis diagnosis. We applied machine learning algorithms to train an endometriosis prediction model. The optimal prediction was achieved with the gradient boosting algorithms of CatBoost for the data-combined model with an area under the ROC curve (ROC-AUC) of 0.81. The same results were obtained for women from a mixed ethnicity population of the UKB (7112; ICD-10: N80). We discovered that, prior to being diagnosed with endometriosis, affected women had significantly more ICD-10 diagnoses than the average unaffected woman. We used SHAP, an explainable AI tool, to estimate the marginal impact of a feature, given all other features. The informative features ranked by SHAP values included irritable bowel syndrome (IBS) and the length of the menstrual cycle. We conclude that the rich population-based retrospective data from the UKB are valuable for developing unified machine learning endometriosis models despite the limitations of missing data, noisy medical input, and participant age. The informative features of the model may improve clinical utility for endometriosis diagnosis.

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Section: Discussionmentioning

confidence: 99%

Revisiting the Risk Factors for Endometriosis: A Machine Learning Approach

Blass

Sahar

Shraibman

et al. 2022

JPM

View full text Add to dashboard Cite

show abstract

“…With the increasing use of medical imaging, videos, and pathological samples, machine learning and deep learning approaches are playing a growing role in diagnosis [55]. A machine learning model for endometriosis based on a screening questionnaire was shown to produce an AUC of 0.5–0.9 in the training and validation sets based on the combination of 16 common criteria such as age, pain, and family history [56].…”

Section: Discussionmentioning

confidence: 99%

Unified Predictive Model for Endometriosis: Merging Clinical, Self-reporting and Genetic Information

Blass

Sahar

Shraibman

et al. 2022

Preprint

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Endometriosis is a condition characterized by implants of endometrial tissues into extrauterine sites, mostly within the pelvic peritoneum. The prevalence of endometriosis is under-diagnosed, and estimated to account for 5-10% of all women of reproductive age. The goal of this study is to develop a model for endometriosis based on the UK-biobank (UKBB). We partitioned the data into those diagnosed with endometriosis (5,924, ICD-10 N80) and a control group (142,576). We included over 1000 variables from UKBB covering personal information about female health, lifestyle, self-reported data, genetic variants, and medical history prior to endometriosis diagnosis. We applied machine learning algorithms to train an endometriosis prediction model. The optimal prediction was achieved with the gradient boosting algorithms of CatBoost for the data-combined model, with an area under the ROC curve (roc-AUC) of 0.78. We discovered that, prior to being diagnosed with endometriosis, women had significantly more ICD-10 diagnoses than the average unaffected woman. Informative features, ranked by SHAP values included irritable bowel syndrome (IBS) and the length of the menstrual cycle. We conclude that the rich population-based retrospective data from the UKBB is valuable for developing predictive models despite the limitations of missing data and noisy medical input. The informative features of the model may improve clinical utility for endometriosis diagnosis.

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“…for polyp detection [11,27,28], to best of our knowledge, no directly comparable work has been conducted regarding image or video-based laparoscopic endometriosis segmentation. When, however, targeting classification we find almost all image-based works are on the different subject of endometrial cancer detection in MRI [38]. Nevertheless, we identify two approaches targeting endometriosis classification, albeit one does not contain any results besides extracting visual features [39].…”

Section: Related Workmentioning

confidence: 99%

Endometriosis detection and localization in laparoscopic gynecology

Leibetseder

Schoeffmann

Keckstein

et al. 2022

Multimed Tools Appl

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Endometriosis is a common gynecologic condition typically treated via laparoscopic surgery. Its visual versatility makes it hard to identify for non-specialized physicians and challenging to classify or localize via computer-aided analysis. In this work, we take a first step in the direction of localized endometriosis recognition in laparoscopic gynecology videos using region-based deep neural networks Faster R-CNN and Mask R-CNN. We in particular use and further develop publicly available data for transfer learning deep detection models according to distinctive visual lesion characteristics. Subsequently, we evaluate the performance impact of different data augmentation techniques, including selected geometrical and visual transformations, specular reflection removal as well as region tracking across video frames. Finally, particular attention is given to creating reasonable data segmentation for training, validation and testing. The best performing result surprisingly is achieved by randomly applying simple cropping combined with rotation, resulting in a mean average segmentation precision of 32.4% at 50-95% intersection over union overlap (64.2% for 50% overlap).

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An Analogy of Endometriosis Recognition Using Machine Learning Techniques

Cited by 13 publications

References 11 publications

Revisiting the Risk Factors for Endometriosis: A Machine Learning Approach

Revisiting the Risk Factors for Endometriosis: A Machine Learning Approach

Unified Predictive Model for Endometriosis: Merging Clinical, Self-reporting and Genetic Information

Endometriosis detection and localization in laparoscopic gynecology

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