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Determination of probability of causative pathogen in infectious keratitis using deep learning algorithm of slit-lamp images

小山 あゆみ 鳥取大学

2022.03.04

概要

令和 4 年 2 月

小山あゆみ


学位論文審査要旨







浩 樹

副主査





幸 次











主論文
Determination of probability of causative pathogen in infectious keratitis using deep
learning algorithm of slit-lamp images
(細隙灯顕微鏡画像の深層学習アルゴリズムを使用した感染性角膜炎の原因病原体の確率
測定)
(著者:小山あゆみ、宮﨑大、中川雄次、綾塚祐二、三宅瞳、江原二三枝、佐々木慎一、
清水由美子、井上幸次)
令和3年

Scientific Reports

11巻

22642

参考論文
1. Nigrospora属糸状菌による遷延性真菌性角膜炎の1例
(著者:小山あゆみ、大谷史江、宮﨑大、井上幸次、室田博美、蝶野郁世、木村圭吾)
令和3年

臨床眼科

75巻

118頁~123頁













Determination of probability of causative pathogen in infectious keratitis using deep
learning algorithm of slit-lamp images
(細隙灯顕微鏡画像の深層学習アルゴリズムを使用した感染性角膜炎の原因病原体の確率
測定)
角膜混濁は失明の重要な原因であり、その主な病因は感染性角膜炎である。細隙灯顕微
鏡検査は、原因となる病原体を特定するための一般的検査である。しかし、経験豊富な眼
科医でも診断精度は高くない。角膜画像認識に顔認識に使用される深層学習アルゴリズム
を適用し、角膜炎を引き起こす特定の病原体、アカントアメーバ、細菌、真菌、herpes
simplex virus (HSV)について病原体確率スコアを推測させた。また同一眼で最大4画像ま
での各病原体確率スコア、病原体分類、蛍光染色照明の使用有無をgradient boosting
decision tree(GBDT)の特徴量として用いて学習させた。作成された深層学習アルゴリズム
は、4疾患すべてにおいて高精度な診断が可能であった。



2005年8月から2020年12月に鳥取大学医学部附属病院を受診し、4疾患のいずれかと診断

された感染性角膜炎画像3994枚、Web上に公開された文献において4疾患のいずれかと診断
された感染性角膜炎画像312枚、合計4306枚を対象とした。受診患者の診断は、適切な薬物
治療への反応性、臨床的特徴および特定の病原体の検出に基づき、3人の眼科医によって検
討し合意を得た症例のみ対象とした。Web文献では診断名が明記されている細隙灯顕微鏡画
像のみを対象とした。画像の種類はスリット、ディフューザー、または蛍光染色照明のい
ずれかで撮影されたものとした。最終的な深層学習モデルは、InceptionResNetV2(画像の
299×299の解像度)に基づき2段階モデルで構築された。同一眼で最大4画像までの各病原
体確率スコア、病原体分類、蛍光染色照明の使用有無をgradient boosting decision
tree(GBDT)の特徴量として用いて学習させた。また、開発初期段階での検証であるが、深
層学習アルゴリズムのパフォーマンス検証のために、アプリケーションソフトウェアであ
る「KeratiTest」を用いて、35人の眼科専門医との診断精度を検証させた。



マルチクラス診断の全体的な精度は88.0%であった。

またグループKフォールド検証における診断精度は、アカントアメーバ97.9%、細菌90.7%、
真菌95.0%、HSV92.3%であった。Receiver Operating Characteristic curve (ROC)分
析におけるArea Under the Curve (AUC)は、アカントアメーバ0.995(95%CI:0.991 – 0.998)、

細菌0.963(95%CI:0.952 – 0.973)、真菌0.975(95%CI:0.964 – 0.984)、HSV0.946
(95%CI:0.926-0.964)であった。
未知のデータセットに対する深層学習アルゴリズムの堅牢性を検証するために、4306枚の
画像すべてをランダムに3882枚のトレーニング画像と424枚のテスト画像に分割し、アルゴ
リズムを初期化し、トレーニング画像を使用して再トレーニングした。同一眼の異なる画
像はトレーニンググループまたはテストグループのいずれかに含まれるように分割した。
診断精度は、アカントアメーバ96.7%、細菌77.6%、真菌84.2%、HSV91.7%であった。AUC
はアカントアメーバ0.995(95%CI:0.989 – 0.999)、細菌0.889(95%CI:0.856 – 0.917)、
真菌0.889(95%CI:0.855 – 0.920)、 HSV0.956(0.933 – 0.974)であった。



細隙灯顕微鏡検査における病原体の診断精度は経験豊富な眼科医でも高くない。実際著

者らが作成した「KeratiTest」では平均約40%の精度であり深層学習アルゴリズムのパフ
ォーマンスをはるかに下回った。
また角膜分野でAI開発が遅れている理由として細隙灯顕微鏡による角膜写真は、スリット
光による画像、ディフューザー画像、蛍光染色照明による画像といった画像種類の多様さ、
また角度や照明のばらつき、画質の違いがあり画像の標準化が困難である点が考えられ、
この点は網膜画像と対照的である。
著者らは角膜画像と類似の性質を持つ画像認識分野としてセキュリティ分野における顔認
識に注目した。顔認識は、トレーニングに使用できる顔の画質や角度、照明が異なる点で
細隙灯顕微鏡による角膜画像と共通している。顔認識分野では深層学習アルゴリズム開発
のためにRing lossというアプローチ法を使用している。Ring lossという「損失関数」を
用いることで特徴量パラメータのばらつきを至適化することに成功し、角膜疾患画像から
感染性角膜炎の病原体を高精度に診断できる深層学習アルゴリズム開発が可能となった。
しかしながら著者らが開発した深層学習アルゴリズムは4疾患に限定されており、異なる病
原種を持つ地域には適用できない可能性があること、汎用性を上げるには感染性または非
感染性を分類する深層学習アルゴリズムの開発が望まれることなどが今後の課題である。



Webを含めた細隙灯顕微鏡画像には、高精度な診断に有用な情報が含まれており、Ring

lossによるアプローチは感染性角膜炎の病原体を高精度に診断できる深層学習アルゴリズ
ム開発を可能にした。深層学習アルゴリズムは経験豊富な眼科医の診断精度を上回った。
深層学習アルゴリズムの開発は、細隙灯顕微鏡写真を自動診断し、早期診断を可能にする
効率的な遠隔医療プラットフォームの確立の基礎となる可能性がある。

この論文で使われている画像

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Acknowledgements

This work was supported by Grant-in-Aid for Scientific Research from the Japanese Ministry of Education, Science, and Culture: 17K11481, 21K09720, and 21K09742.

Author contributions

A.K. and D.M. designed the research study and analyzed data. A.K. and D.M. wrote the manuscript. A.K. and

Y.S. performed experiments. D.M., Y.N., and Y.A. wrote the code for deep learning models and GBDT. A.K.,

D.M., H.M., F.E., S.S., and Y.I. conducted evaluation on diagnosis of clinical images. Y.I. supervised experiments

and data analysis. All of the authors approved the manuscript to be published and agreed to be accountable for

all aspects of the study.

Competing interests Dai Miyazaki reports lecture fee from Santen Pharmaceutical, Senju Pharmaceutical, Alcon, outside the submitted work. Funding: Grant-in-Aid for Scientific Research from the Japanese Ministry of Education, Science, and

Culture, 21K09742. Yoshitsugu Inoue reports grants and lecture fee from Senju Pharmaceutical Co, Ltd., grants

from Santen Pharmaceutical Co, Ltd., grants from Alcon Japan, Ltd., outside the submitted work. Funding:

Grant-in-Aid for Scientific Research from the Japanese Ministry of Education, Science, and Culture, 17K11481,

21K09720. Ayumi Koyama, Yuji Nakagawa, Yuji Ayatsuka, Hitomi Miyake, Fumie Ehara, Shin-ichi Sasaki, and

Yumiko Shimizu declare no competing interests.

Additional information

Supplementary Information The online version contains supplementary material available at https://​doi.​org/​

10.​1038/​s41598-​021-​02138-w.

Correspondence and requests for materials should be addressed to D.M.

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