To develop a machine learning method for the automatic recognition of endometriosis lesions during laparoscopic surgery and evaluate its feasibility and performance. Collecting and annotating surgical videos and training, validating, and testing a deep neural network. Multicenter proof-of-concept study using surgical videos from expert centers in France, Hungary, Brazil, and Denmark. Surgical video sequences were collected from 112 patients who underwent laparoscopic procedures for suspected endometriosis between January 2020 and August 2023. Sequences with identifiable endometriosis lesions were included, while poor-quality images and sequences with prior surgical manipulation were excluded. A deep neural network based on YOLOv5 was trained to detect and classify nine visual classes of endometriosis lesions (superficial black, superficial red, superficial white, superficial subtle, filmy adhesions, dense adhesions, deep endometriosis, ovarian endometrioma, and ovarian chocolate fluid). The model performance was good for the 'superficial black', 'superficial subtle', and 'ovarian chocolate fluid' classes (F1 score = 0.94, 0.74, and 0.75, respectively), acceptable for the 'dense adhesion', 'ovarian endometrioma' and 'deep endometriosis' classes (F1 score = 0.70, 0.63 and 0.632, respectively), and weak for the 'superficial red', 'superficial white', and 'filmy adhesions' classes (F1 score = 0.25, 0.18, 0.16 and 0.02, respectively). However, while these results highlight the model's strong potential in identifying most lesions in at least one frame of each sequence, they underscore the need for further refinement to improve accuracy and precision. This study demonstrates the feasibility of applying artificial intelligence for visual recognition of endometriosis during laparoscopic surgery. While the initial results are encouraging, further development is needed to enhance the model performance and standardize the annotation methods. The integration of AI in surgical practice holds promise for assisting in endometriosis diagnosis and improving surgical outcomes.

Background: Laparoscopic surgery for endometriosis presents unique challenges due to the complexity of and variability in lesion appearances within the abdominal cavity. This study investigates the application of deep learning models for object detection in laparoscopic videos, aiming to assist surgeons in accurately identifying and localizing endometriosis lesions and related anatomical structures. A custom dataset was curated, comprising of 199 video sequences and 205,725 frames. Of these, 17,560 frames were meticulously annotated by medical professionals. The dataset includes object detection annotations for 10 object classes relevant to endometriosis, alongside segmentation masks for some classes. Methods: To address the object detection task, we evaluated the performance of two deep learning models—FasterRCNN and YOLOv9—under both stratified and non-stratified training scenarios. Results: The experimental results demonstrated that stratified training significantly reduced the risk of data leakage and improved model generalization. The best-performing FasterRCNN object detection model achieved a high average test precision of 0.9811 ± 0.0084, recall of 0.7083 ± 0.0807, and mAP50 (mean average precision at 50% overlap) of 0.8185 ± 0.0562 across all presented classes. Despite these successes, the study also highlights the challenges posed by the weak annotations and class imbalances in the dataset, which impacted overall model performances. Conclusions: In conclusion, this study provides valuable insights into the application of deep learning for enhancing laparoscopic surgical precision in endometriosis treatment. The findings underscore the importance of robust dataset curation and advanced training strategies in developing reliable AI-assisted tools for surgical interventions. The latter could potentially improve the guidance of surgical interventions and prevent blind spots occurring in difficult to reach abdominal regions. Future work will focus on refining the dataset and exploring more sophisticated model architectures to further improve detection accuracy.

To evaluate the relationship between genetic haplotypes associated with celiac disease (Human Leucocyte Antigen [HLA] DQ2 and DQ8) with the diagnosis, clinical presentation, and location of endometriosis in Brazilian women. A retrospective cross-sectional study, was conducted in a Tertiary hospital. Women aged 18-50 years who underwent HLA-DQ2 and HLA-DQ8 haplotype analysis. The patients were divided into endometriosis and control groups and evaluated for symptoms; endometriosis location, American Society for Reproductive Medicine (ASRM) stage, and the presence of anti-tissue transglutaminase IgA (anti-TgA), HLA-DQ2, and HLA-DQ8 markers. A total of 434 consecutive patients with (n = 315) and without (n = 119) endometriosis were included. Pain and infertility were more frequent in the endometriosis group than in the control group. The presence of HLA-DQ2, HLA-DQ8, and anti-TgA was similar between both groups. The presence of HLA-DQ2 and HLA-DQ8 markers did not differ based on age, pain symptoms, ASRM stage, or endometriosis location. Although there are similarities in inflammatory markers and pathophysiology between celiac disease and endometriosis, this study found no significant associations in the presence of HLA-DQ2 or HLA-DQ8 haplotypes and endometriosis.

Laparoscopic surgery for endometriosis presents unique challenges due to the complexity and variability of lesion appearances within the abdominal cavity. This study investigates the application of deep learning models for object detection in laparoscopic videos, aiming to assist surgeons in accurately identifying and localising endometriosis lesions and related anatomical structures. A custom dataset was curated, comprising 332 video sequences and 354,906 frames. Of these, 17,560 frames were meticulously annotated by medical professionals. The dataset includes object-detection annotations for 10 object classes relevant to endometriosis, alongside segmentation masks for some classes. To address the object detection task, we evaluated the performance of three deep learning models - FasterRCNN, MaskRCNN, and YOLOv9 - under both stratified and non-stratified training scenarios. Experimental results demonstrated that stratified training significantly reduced the risk of data leakage and improved model generalization. The best-performing object detection model achieved high precision and recall across most classes. Despite these successes, the study also highlights the challenges posed by the weak annotations and class imbalances in the dataset, which impacted overall models performances. In conclusion, this study provides valuable insights into the application of deep learning for enhancing laparoscopic surgical precision in endometriosis treatment. The findings underscore the importance of robust dataset curation and advanced training strategies in developing reliable AI-assisted tools for surgical interventions. Future work will focus on refining the dataset and exploring more sophisticated model architectures to further improve detection accuracy.