Classification of Images Acquired with Colposcopy Using Artificial Neural Networks

Abstract

OBJECTIVETo explore the advantages of using artificial neural networks (ANNs) to recognize patterns in colposcopy to classify images in colposcopy.PURPOSETransversal, descriptive, and analytical study of a quantitative approach with an emphasis on diagnosis. The training test e validation set was composed of images collected from patients who underwent colposcopy. These images were provided by a gynecology clinic located in the city of Criciúma (Brazil). The image database (n = 170) was divided; 48 images were used for the training process, 58 images were used for the tests, and 64 images were used for the validation. A hybrid neural network based on Kohonen self-organizing maps and multilayer perceptron (MLP) networks was used.RESULTSAfter 126 cycles, the validation was performed. The best results reached an accuracy of 72.15%, a sensibility of 69.78%, and a specificity of 68%.CONCLUSIONAlthough the preliminary results still exhibit an average efficiency, the present approach is an innovative and promising technique that should be deeply explored in the context of the present study.

Feasibility of deep learning for predicting live birth from a blastocyst image in patients classified by age

“…In several reports, deep learning with convolutional neural networks as AI has been used in medicine . The accuracies of this method with deep learning have been published and were 0.997 for histopathological diagnosis of breast cancer, 0.90‐0.83 for the early diagnosis of Alzheimer's disease, 0.83 for urological dysfunctions, 0.72 and 0.50 for colposcopy, 0.83 for the diagnostic imaging of orthopedic trauma, and 0.98 for the morphological quality of blastocysts and evaluation by embryologist . In one report, embryos with fair‐quality images that were classified as poor and good quality were scored as 0.509 and 0.614, respectively, for the likelihood of achieving a positive live birth .…”

Feasibility of artificial intelligence for predicting live birth without aneuploidy from a blastocyst image

“…Because there are 160 images in this study, we studied machine learning except deep learning. The accuracies with the deep learning have been reported such as 0.997 for histopathological diagnosis of breast cancer , 0.90‐0.83 for the early diagnosis of the Alzheimer's disease , 0.83 for urological dysfunctions , 0.72 , and 0.50 for the colposcopy, 0.83 for the diagnostic imaging of orthopedic trauma . In practical sterility, there are some clinical disincentives for the embryo to achieve live birth; the uterine factors —(intrauterine adhesions , uterine myomas , and endometrial polyps), endometriosis , ovarian function , oviduct obstruction , female diseases such as diabetes mellitus , immune disorder , and uterine microbiota .…”

Application of deep learning to the classification of uterine cervical squamous epithelial lesion from colposcopy images combined with HPV types

“…In medicine, several studies have used AI for deep learning with convolutional neural networks (48,49). The accuracy values of AI with deep learning have been published and include 0.997 for the histopathological diagnosis of breast cancer (50), 0.980 for the morphological quality of blastocysts and evaluation by an embryologist (51), 0.640-0.880 for predicting live birth from a blastocyst image of patients by age (4,52), 0.650 for predicting live birth without aneuploidy from a blastocyst image (53), 0.823 (3), 0.720 (54) and 0.500 (55) for colposcopy, 0.830 to 0.900 for the early diagnosis of Alzheimer's disease (56), 0.830 for urological dysfunctions (57) and 0.830 for the diagnostic imaging of orthopedic trauma (58). A number of studies have reported a limitation of conventional colposcopy.…”