Automated Detection of Endometrial Polyps from Hysteroscopic Videos Using Deep Learning

Zhao, Aihua; Du, Xin; Yuan, Shengjie; Shen, Wenfeng; Zhu, Xin; Wang, Wenwen

doi:10.3390/diagnostics13081409

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…Zhao et al developed a DL model to automatically detect only endometrial polyps in real-time hysteroscopic videos with an accuracy of up to 95%; unfortunately, they did not perform any classification of the lesions [25].…”

Section: Discussionmentioning

confidence: 99%

Detection and Classification of Hysteroscopic Images Using Deep Learning

Raimondo,

Raffone,

Salucci

et al. 2024

Cancers

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Background: Although hysteroscopy with endometrial biopsy is the gold standard in the diagnosis of endometrial pathology, the gynecologist experience is crucial for a correct diagnosis. Deep learning (DL), as an artificial intelligence method, might help to overcome this limitation. Unfortunately, only preliminary findings are available, with the absence of studies evaluating the performance of DL models in identifying intrauterine lesions and the possible aid related to the inclusion of clinical factors in the model. Aim: To develop a DL model as an automated tool for detecting and classifying endometrial pathologies from hysteroscopic images. Methods: A monocentric observational retrospective cohort study was performed by reviewing clinical records, electronic databases, and stored videos of hysteroscopies from consecutive patients with pathologically confirmed intrauterine lesions at our Center from January 2021 to May 2021. Retrieved hysteroscopic images were used to build a DL model for the classification and identification of intracavitary uterine lesions with or without the aid of clinical factors. Study outcomes were DL model diagnostic metrics in the classification and identification of intracavitary uterine lesions with and without the aid of clinical factors. Results: We reviewed 1500 images from 266 patients: 186 patients had benign focal lesions, 25 benign diffuse lesions, and 55 preneoplastic/neoplastic lesions. For both the classification and identification tasks, the best performance was achieved with the aid of clinical factors, with an overall precision of 80.11%, recall of 80.11%, specificity of 90.06%, F1 score of 80.11%, and accuracy of 86.74 for the classification task, and overall detection of 85.82%, precision of 93.12%, recall of 91.63%, and an F1 score of 92.37% for the identification task. Conclusion: Our DL model achieved a low diagnostic performance in the detection and classification of intracavitary uterine lesions from hysteroscopic images. Although the best diagnostic performance was obtained with the aid of clinical data, such an improvement was slight.

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Section: Discussionmentioning

confidence: 99%

Detection and Classification of Hysteroscopic Images Using Deep Learning

Raimondo,

Raffone,

Salucci

et al. 2024

Cancers

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“…Although YOLOX inherits the low accuracy problems in the presence of insulator defect detection as well as the vulnerability of the detection results to interference by complex backgrounds, YOLOX has faster speed and higher accuracy in object detection compared with the ancestral versions [ 94 ]. Zhao et al [ 95 ] adopted a modified YOLOX model for accurate identification of endometrial polyps in hysteroscopic video images. They combined the model with the group normalization method, of which characterization lies in the calculation of the mean and standard deviation for normalization using the set of pixels based on four-dimensional vectors (batch axis, channel axis, and spatial axes of height and width).…”

Section: Application Of Cnn Model To Hysteroscopic Image Analysismentioning

confidence: 99%

Bridging the Diagnostic Gap between Histopathologic and Hysteroscopic Chronic Endometritis with Deep Learning Models

Kitaya,

Yasuo,

Yamaguchi

2024

Medicina

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Chronic endometritis (CE) is an inflammatory pathologic condition of the uterine mucosa characterized by unusual infiltration of CD138(+) endometrial stromal plasmacytes (ESPCs). CE is often identified in infertile women with unexplained etiology, tubal factors, endometriosis, repeated implantation failure, and recurrent pregnancy loss. Diagnosis of CE has traditionally relied on endometrial biopsy and histopathologic/immunohistochemistrical detection of ESPCs. Endometrial biopsy, however, is a somewhat painful procedure for the subjects and does not allow us to grasp the whole picture of this mucosal tissue. Meanwhile, fluid hysteroscopy has been recently adopted as a less-invasive diagnostic modality for CE. We launched the ARCHIPELAGO (ARChival Hysteroscopic Image-based Prediction for histopathologic chronic Endometritis in infertile women using deep LeArninG mOdel) study to construct the hysteroscopic CE finding-based prediction tools for histopathologic CE. The development of these deep learning-based novel models and computer-aided detection/diagnosis systems potentially benefits infertile women suffering from this elusive disease.

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