Male infertility scoring using AI-assisted image classification requiring no programming

All images are magnified 400 times. Algorithm performance with Google Cloud AutoML Vision, average precision recall curve for image dataset, magnification X400. Photo credit: Hideyuki Kobayashi

One of Dr. Hideyuki Kobayashi-led research group at Toho University’s Omori Medical Center in Tokyo developed an AI-based image classifier that provides scores for histological testicular images of patients with azoospermia. The goal of urologist Dr. Kobayashi was to develop a user-friendly pathological examination method for daily clinical practice. This enabled testicular images to be classified with an accuracy of 82.6%.

Infertility affects women and men equally. In male infertility, azoospermia (the lack of sperm in semen) is a major problem that prevents a couple from having a child. For the treatment of patients with azoospermia, testicular sperm extraction (TESE) is required to obtain mature sperm. Upon examination, histological specimens are typically graded using the Johnsen score on a scale of 1 to 10 based on the histopathological features of the testicle.

“The Johnsen Score has been widely used in urology since it was first reported 50 years ago. However, histopathological assessment of the testicle is not an easy task and takes a long time due to the complexity of testicular tissue resulting from the many highly specialized steps involved Our goal was to simplify this time-consuming diagnostic step by leveraging AI technology, so we chose Google’s vision for automated machine learning (AutoML), which does not require a program to create an AI model for the individual Patients create datasets. AutoML Vision allows clinicians with no programming skills to use deep learning to create their own models without the assistance of data scientists, “said Dr. Hideyuki Kobayashi, Associate Professor of Urology at Toho University School of Medicine (Fig. 1). .

“The model we created can classify histological images of the testicle without the help of pathologists. I hope our approach will enable clinicians in all areas of medicine to create AI-based models that can be used in their daily clinical practice,” he said.

Male infertility assessment using AI-assisted image classification that does not require programming

Johnsen scores and classification of four labels used in the study. Photo credit: Hideyuki Kobayashi

To simplify the use of Johnsen scores in clinical practice, Dr. Kobayashi four designations: Johnsen score 1–3, 4–5, 6–7 and 8–10 (Fig. 2). He and his co-researchers obtained a data set of 7155 images at 400x magnification. All images were uploaded to the Google Cloud AutoML Vision platform. For the X400 magnification image dataset, the average accuracy (positive predictive value) of the algorithm was 82.6%, the accuracy was 80.31%, and the recall was 60.96% (Fig. 3).

AI has become popular and is used in all areas of medicine. However, the use of AI by clinicians in hospitals is still hampered by the need for help from data scientists in properly using AI. “The cloud-based machine learning framework we’ve been using is for everyone. It can become such a powerful tool in medicine that in the near future hospital doctors will be able to use AI-based medical image classifiers the same way they do will be using Microsoft PowerPoint or Excel now, “said Dr. Kobayashi. He added, “The hardest part was taking pictures of the testicular pathology and it was very time consuming. Two colleagues worked very hard to get all of the images used in the study. I really appreciate their dedicated efforts.”

Dr. Kobayashi has described the development of an AI-based algorithm for evaluating Johnsen scores, in which original images (X400) were combined to achieve a high level of accuracy. This is the first report of an algorithm that can predict Johnsen scores without relying on pathologists and data science experts.

The study looked at sperm production in men with testicular cancer

More information:
Yurika Ito et al., A Method of Using Automated Machine Learning to Histopathological Classification of Testes Based on Johnsen Scores, Scientific Reports (2021). DOI: 10.1038 / s41598-021-89369-z

Provided by Toho University

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