光干渉断層計画像から緑内障の視野を推測する３次元畳み込みニューラルネットワークモデルの構築
DOI:
https://doi.org/10.51094/jxiv.170キーワード:
光干渉断層計、 緑内障、 視野、 深層学習抄録
目的: 光干渉断層計（OCT）画像から視野を推定し、その精度を向上させるため、症例数を増やす事が困難な従来の2次元モデルに代えて、セグメンテーション不要の3次元畳み込みニューラルネットワークモデル（3DCNN）により学習を行った。
方法： 単一施設の後ろ向き全例調査である。OCTに加えハンフリー視野計(HFA) 24-2もしくは10-2検査を受けた、すべてのタイプの緑内障または緑内障の疑いの患者を含む連続3,416人（6,356眼）を調査した。学習すべき視野及び視野の傾きの教師値は、視野閾値の測定点毎の回帰直線から算出した。学習対象と評価対象を分け、学習時に低signal strength index(SSI)及び眼内疾患合併の有無で４群に分けて3DCNNモデルによる５分割交差検証を施行し、同一条件の評価対象で最良の結果が得られた学習条件で10分割交差検証を施行した。
結果:５分割交差検証の結果、全てのSSI及び眼内疾患を含んで学習した群がいずれの対象に対しても最も推測性能が高く、10分割交差検証の結果、緑内障患者のペアデータあたりの二乗平均平方根誤差は、HFA24-2で3.27±2.20dB［平均 ± 標準偏差］、HFA10-2で3.02±2.28dBであった。
結論: 今回のモデルにより、OCT画像からの高い視野推測精度を達成した。容易に症例数を増やす事ができるため、今後の多施設共同研究が期待される。
臨床への橋渡し：本モデルは、OCT画像から高い精度で視野を推定できるため、患者の負担を軽減することが可能である。
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投稿日時: 2022-09-20 04:54:45 UTC
公開日時: 2022-09-27 00:12:15 UTC — 2022-11-10 23:40:10 UTCに更新
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改版理由
症例選択のフローチャートを追加しました。また、表２の中に、HFAの33%のカットオフの前の症例及び、表１の症例を一部含んでしまっていたため、表２の疾患を数え直して訂正しました。また、説明が不十分であったため全体的に丁寧に文章を再構成して記載し直しました。ライセンス
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古山, 誠
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