An artificial intelligence‑assisted diagnostic system improves the accuracy of image diagnosis of uterine cervical lesions

Ito,Yu; Miyoshi,Ai; Ueda,Yutaka; Tanaka,Yusuke; Nakae,Ruriko; Morimoto,Akiko; Shiomi,Mayu; Enomoto,Takayuki; Sekine,Masayuki; Sasagawa,Toshiyuki; Yoshino,Kiyoshi; Harada,Hiroshi; Nakamura,Takafumi; Murata,Takuya; Hiramatsu,Keizo; Saito,Junko; Yagi,Junko; Tanaka,Yoshiaki; Kimura,Tadashi

doi:10.3892/mco.2021.2460

February-2022 Volume 16 Issue 2

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February-2022 Volume 16 Issue 2

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Article

An artificial intelligence‑assisted diagnostic system improves the accuracy of image diagnosis of uterine cervical lesions

Authors:
- Yu Ito
- Ai Miyoshi
- Yutaka Ueda
- Yusuke Tanaka
- Ruriko Nakae
- Akiko Morimoto
- Mayu Shiomi
- Takayuki Enomoto
- Masayuki Sekine
- Toshiyuki Sasagawa
- Kiyoshi Yoshino
- Hiroshi Harada
- Takafumi Nakamura
- Takuya Murata
- Keizo Hiramatsu
- Junko Saito
- Junko Yagi
- Yoshiaki Tanaka
- Tadashi Kimura
View Affiliations / Copyright

Affiliations: Department of Obstetrics and Gynecology, Osaka University Graduate School of Medicine, Suita, Osaka 567‑0871, Japan, Department of Obstetrics and Gynecology, Niigata University Graduate School of Medicine, Chuo-ku, Niigata 951‑8520, Japan, Department of Obstetrics and Gynecology, Kanazawa Medical University, Uchinada, Ishikawa 920‑0293, Japan, Department of Obstetrics and Gynecology, University of Occupational and Environmental Health, Kitakyushu, Fukuoka 807‑8556, Japan, Department of Obstetrics and Gynecology, Kawasaki Medical University, Kurashiki, Okayama 701‑0192, Japan, Hiramatsu Obstetrics and Gynecology Clinic, Kishiwada-shi, Osaka 583‑0024, Japan, Saito Women Clinic, Yodogawa-ku, Osaka 532‑0003, Japan, Ladies Clinic Yagi, Senboku-gunn, Osaka 595‑0805, Japan, Maki Ladies Clinic, Ibaraki-shi, Osaka 567‑0031, Japan
Article Number: 27
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Published online on: December 8, 2021

https://doi.org/10.3892/mco.2021.2460
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Abstract

The present study created an artificial intelligence (AI)‑automated diagnostics system for uterine cervical lesions and assessed the performance of these images for AI diagnostic imaging of pathological cervical lesions. A total of 463 colposcopic images were analyzed. The traditional colposcopy diagnoses were compared to those obtained by AI image diagnosis. Next, 100 images were presented to a panel of 32 gynecologists who independently examined each image in a blinded fashion and diagnosed them for four categories of tumors. Then, the 32 gynecologists revisited their diagnosis for each image after being informed of the AI diagnosis. The present study assessed any changes in physician diagnosis and the accuracy of AI‑image‑assisted diagnosis (AISD). The accuracy of AI was 57.8% for normal, 35.4% for cervical intraepithelial neoplasia (CIN)1, 40.5% for CIN2‑3 and 44.2% for invasive cancer. The accuracy of gynecologist diagnoses from cervical pathological images, before knowing the AI image diagnosis, was 54.4% for CIN2‑3 and 38.9% for invasive cancer. After learning of the AISD, their accuracy improved to 58.0% for CIN2‑3 and 48.5% for invasive cancer. AI‑assisted image diagnosis was able to improve gynecologist diagnosis accuracy significantly (P<0.01) for invasive cancer and tended to improve their accuracy for CIN2‑3 (P=0.14).

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Journals

International Journal of Molecular Medicine

International Journal of Oncology

Molecular Medicine Reports

Oncology Reports

Experimental and Therapeutic Medicine

Oncology Letters

Biomedical Reports

Molecular and Clinical Oncology

World Academy of Sciences Journal

International Journal of Functional Nutrition

International Journal of Epigenetics

Medicine International

An artificial intelligence‑assisted diagnostic system improves the accuracy of image diagnosis of uterine cervical lesions

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Abstract

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