Prospective Studies on Artificial Intelligence for Cancer Pathology Evaluation
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 10
- 试验地点
- 1
- 主要终点
- Diagnostic performance of APL detection
研究概览
简要总结
This study will test whether artificial intelligence (AI) can help doctors diagnose a rare blood cancer called acute promyelocytic leukemia (APL) more quickly and accurately. Doctors usually examine bone marrow samples under a microscope to make this diagnosis, but it can be challenging and time-consuming.
In this study, doctors will review bone marrow samples under three different conditions:
- Unaided Review: Without AI assistance.
- AI as Double-Check: AI-generated evaluation shown after the doctor makes an initial decision.
- AI as First Look: AI-generated evaluation shown at the start of the review.
Doctors will be randomly assigned to different orders of these three conditions. This design will allow us to compare how AI support affects diagnostic accuracy, speed, and confidence.
详细描述
This study aims to evaluate the effect of artificial intelligence (AI) assistance on clinicians' diagnostic performance in detecting acute promyelocytic leukemia (APL) using Wright-Giemsa-stained bone marrow whole-slide images (WSIs). The Leukemia End-to-End Analysis Platform (LEAP) will serve as the AI model under assessment.
This is a single-session, within-reader study. Participants will be randomly assigned to one of two study arms, which differ in the order of diagnostic blocks:
* Arm 1 (X -> Y): Block X (Unaided Review): Clinicians review WSIs without AI support. Diagnostic accuracy, time to decision, and confidence will be recorded.
Block Y (AI-Assisted Review): Comprising two sub-blocks presented in randomized order:
Y1 (AI as Double-Check): Clinicians provide an initial diagnosis and confidence score without the aid of AI. AI predictions are then revealed, and clinicians may revise their diagnosis. Both pre-AI and post-AI decisions will be recorded.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Crossover
- 主要目的
- Diagnostic
- 盲法
- Triple (Care Provider, Investigator, Outcomes Assessor)
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •for Pathology Slides (i.e., Cases):
- •Wright-Giemsa-stained bone marrow aspirate smears
- •Final diagnosis confirmed through molecular testing in conjunction with expert pathology evaluation
排除标准
- •for Pathology Slides (i.e., Cases):
- •Poor-quality or unreadable slides
- •Cases used in AI training
- •Inclusion Criteria for Readers (i.e., Participants):
- •Board-certified or board-eligible pathologists, or board-certified/board-eligible hematologists who routinely make hematopathology diagnoses in their clinical practice
- •Willingness to complete both unaided and AI-assisted review sessions
研究组 & 干预措施
Unaided Review First, Then AI-Assisted Review
Readers first complete Block X (Unaided) on their assigned subset SX (34 slides). They then complete Block Y (AI-Assisted) on two separate subsets: SY1 (34 slides; AI as Double-Check) and SY2 (34 slides; AI as First Look). Within Block Y, the order of Y1 and Y2 is randomized. For each reader, SX, SY1, and SY2 are disjoint and stratified by APL status.
干预措施: Unaided Review First, Then AI-Assisted Review (Behavioral)
AI-Assisted Review First, Then Unaided Review
Readers first complete Block Y (AI-Assisted) on two assigned subsets: SY1 (34 slides; AI as Double-Check) and SY2 (34 slides; AI as First Look), with the order of Y1 and Y2 randomized. They then complete Block X (Unaided) on subset SX (34 slides). For each reader, SX, SY1, and SY2 are disjoint and stratified by APL status.
干预措施: AI-Assisted Review First, Then Unaided Review (Behavioral)
结局指标
主要结局
Diagnostic performance of APL detection
时间窗: Periprocedural (at the time of slide review)
Performance of clinicians (unaided and AI-assisted) in detecting APL, measured in accuracy, sensitivity, specificity, positive predictive value, and negative predictive value.
次要结局
- Time to diagnosis(Periprocedural (at the time of slide review))
- Inter-observer variability(Periprocedural (at the time of slide review))
- Concordance between AI predictions and clinicians' diagnoses(Periprocedural (at the time of slide review))
- Decision-change rates(Periprocedural (at the time of slide review))
- Net benefit after AI exposure(Periprocedural (at the time of slide review))
- Clinician confidence level(Periprocedural (at the time of slide review))
研究者
Kun-Hsing Yu
Associate Professor of Biomedical Informatics
Harvard Medical School (HMS and HSDM)