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Detection and Classification of Hysteroscopic Images Using Deep Learning.

Diego Raimondo¹, Antonio Raffone^2,3, Paolo Salucci²

¹Division of Gynaecology and Human Reproduction Physiopathology, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy.

|April 13, 2024

Summary

Deep learning models show limited diagnostic performance for detecting endometrial pathologies from hysteroscopy images. Incorporating clinical factors slightly improved the artificial intelligence tool's accuracy in identifying intrauterine lesions.

Keywords:

carcinoma endometrial cancer endometrial hyperplasia endometrium endoscopy fibroids malignancy minimally invasive neoplasia polyps uterus

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Area of Science:

Gynecology
Artificial Intelligence
Medical Imaging

Background:

Hysteroscopy with endometrial biopsy is standard for diagnosing endometrial pathology, but relies heavily on gynecologist expertise.
Deep learning (DL) offers potential to enhance diagnostic accuracy by analyzing hysteroscopic images.
Limited research exists on DL model performance for intrauterine lesion identification and the impact of clinical data.

Purpose of the Study:

To develop and evaluate a DL model for automated detection and classification of endometrial pathologies using hysteroscopic images.
To assess the diagnostic performance of the DL model with and without the integration of clinical factors.

Main Methods:

A retrospective cohort study analyzed 1500 hysteroscopic images from 266 patients with confirmed intrauterine lesions.
A DL model was developed to classify and identify intracavitary uterine lesions.
Model performance was evaluated with and without the inclusion of patient clinical factors.

Main Results:

The DL model demonstrated low overall diagnostic performance in detecting and classifying lesions.
Performance metrics included precision, recall, F1 score, and accuracy for both classification and identification tasks.
The inclusion of clinical factors resulted in a slight improvement in the DL model's diagnostic performance.

Conclusions:

The developed DL model showed limited diagnostic capability for hysteroscopic endometrial pathology.
While clinical data integration offered a marginal performance enhancement, it did not significantly elevate the model's diagnostic utility.
Further research is needed to improve DL model accuracy for endometrial lesion analysis.