Artificial Intelligence-Assisted Lesion-Based Urgent Referral Triage of Ultra-Widefield Retinal Images: A Multi-Reader Multi-Case Randomized Reader Study (ALERT-UWF)
Clinical Utility of an Artificial Intelligence-Assisted Lesion-Based Urgent Referral Triage System for Ultra-Widefield Retinal Images: A Prospective Multi-Reader Multi-Case Randomized Reader Study
his study evaluates the clinical utility of an artificial intelligence (AI)-assisted lesion-based urgent referral triage system for ultra-widefield (UWF) retinal images.
Unlike disease-classification systems, the AI system identifies predefined vision-threatening retinal findings and generates lesion-level urgent referral recommendations. Participating ophthalmologists will evaluate UWF retinal images under randomized AI-assisted and unassisted conditions.
The primary objective is to determine whether lesion-based AI assistance improves urgent referral triage performance compared with unaided image interpretation.
調査の概要
状態
条件
詳細な説明
Ultra-widefield retinal imaging is increasingly used for retinal disease screening and referral triage. Many vision-threatening retinal abnormalities require timely identification and referral to retinal specialists.
The AI system evaluated in this study is designed as a lesion-based triage tool rather than a disease-diagnosis system. The model identifies predefined urgent referral retinal findings and generates referral recommendations based on lesion-level evidence.
Urgent referral findings include:
- Retinal detachment
- Untreated retinal tear or retinal hole
- Vitreous hemorrhage
- Pre-retinal hemorrhage
- Subretinal hemorrhage
- Retinal neovascularization
- Optic disc neovascularization
- Tractional fibrovascular membrane Treated retinal tears associated with laser barricade scars are classified as non-urgent referral findings.
A total of 600 UWF retinal images acquired using Zeiss and Optos imaging systems will be included.
Participating ophthalmologists will independently evaluate images in randomized AI-assisted and unassisted settings.
The primary objective is to determine whether AI assistance improves lesion-based urgent referral triage accuracy.
研究の種類
入学 (推定)
段階
- 適用できない
連絡先と場所
研究連絡先
- 名前：Xiuju Chen, md
- 電話番号：+8618060955810
- メール：joyychen@aliyun.com
参加基準
適格基準
就学可能な年齢
- 大人
- 高齢者
健康ボランティアの受け入れ
説明
Inclusion Criteria:
- Licensed ophthalmologists
- Willing to participate as readers
- Completion of study training
Exclusion Criteria:
- Retinal specialists involved in establishing gold-standard labels
- Prior access to gold-standard labels
- Incomplete study participation
研究計画
研究はどのように設計されていますか？
デザインの詳細
- 主な目的：診断
- 割り当て：ランダム化
- 介入モデル：階乗代入
- マスキング：なし（オープンラベル）
武器と介入
参加者グループ / アーム |
介入・治療 |
|---|---|
|
実験的：AI-Assisted Interpretation
Readers interpret UWF retinal images with lesion-level AI findings and urgent referral recommendations.
|
Readers interpret UWF retinal images with lesion-level AI findings and urgent referral recommendations.
|
|
アクティブコンパレータ：Unassisted Interpretation
Readers interpret UWF retinal images without AI assistance.
|
Readers interpret UWF retinal images without AI assistance.
|
この研究は何を測定していますか？
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
|
Correct Lesion-Based Urgent Referral Triage Rate
時間枠：Immediately after image interpretation.
|
Proportion of reader referral decisions consistent with expert-adjudicated lesion-based urgent referral classifications.
|
Immediately after image interpretation.
|
二次結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
|
Sensitivity for Urgent Referral Findings
時間枠：Immediately after image interpretation.
|
Sensitivity for Urgent Referral Findings
|
Immediately after image interpretation.
|
|
Specificity for Urgent Referral Findings
時間枠：Immediately after image interpretation.
|
Specificity for correctly classifying non-urgent referral images according to expert-adjudicated lesion-based triage labels.
|
Immediately after image interpretation.
|
|
False-Negative Rate for Urgent Referral Findings
時間枠：Immediately after image interpretation.
|
Proportion of urgent referral images incorrectly classified as non-urgent referral by readers.
|
Immediately after image interpretation.
|
|
False-Positive Rate for Urgent Referral Findings
時間枠：Immediately after image interpretation.
|
Proportion of non-urgent referral images incorrectly classified as urgent referral by readers.
|
Immediately after image interpretation.
|
|
Reader Confidence Score
時間枠：Immediately after image interpretation.
|
Reader-reported confidence level for referral decisions measured using a 5-point Likert scale, ranging from 1 (very uncertain) to 5 (very confident).
|
Immediately after image interpretation.
|
|
Change in Correct Urgent Referral Decisions After AI Assistance
時間枠：Immediately after image interpretation.
|
Number and proportion of cases in which AI assistance changed an incorrect referral decision to a correct referral decision.
|
Immediately after image interpretation.
|
協力者と研究者
研究記録日
主要日程の研究
研究開始 (推定)
一次修了 (推定)
研究の完了 (推定)
試験登録日
最初に提出
QC基準を満たした最初の提出物
最初の投稿 (実際)
学習記録の更新
投稿された最後の更新 (実際)
QC基準を満たした最後の更新が送信されました
最終確認日
詳しくは
本研究に関する用語
キーワード
その他の研究ID番号
- XMYKZX-KY-2026-011
個々の参加者データ (IPD) の計画
個々の参加者データ (IPD) を共有する予定はありますか?
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