AI-Assisted Acute Myeloid Leukemia Evaluation With the Leukemia End-to-End Analysis Platform (LEAP) Versus Clinician-Only Assessment

NCT ID: NCT07203885

Last Updated: 2025-10-31

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Clinical Phase

NA

Total Enrollment

10 participants

Study Classification

INTERVENTIONAL

Study Start Date

2025-09-09

Study Completion Date

2025-10-10

Brief Summary

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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.

Detailed Description

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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.

Y2 (AI as First Look): Clinicians review WSIs with AI-predicted diagnoses visible from the beginning.

\* Arm 2 (Y -\> X): Block Y (AI-Assisted Review): Sub-blocks Y1 and Y2 presented in randomized order.

Block X (Unaided Review): As described above.

Each clinician will review 102 de-identified WSIs. For each reader, slides will be randomly divided into three disjoint subsets (e.g. 34/34/34), stratified by APL status, and assigned to Block X (Unaided), Block Y1 (AI as Double-Check), or Block Y2 (AI as First Look). No slide will be shown to the same reader in more than one block.

In addition, the AI system will independently generate diagnostic predictions for all WSIs to enable benchmarking; however, this does not constitute a participant arm.

Ground-truth diagnoses will be determined by molecular confirmation and expert consensus.

Conditions

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Acute Promyelocytic Leukemia (APL) Acute Myeloid Leukaemia (AML)

Keywords

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AI-Assisted Detection Acute Promyelocytic Leukemia Bone Marrow Slide Whole-slide images Acute Myeloid Leukemia

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

CROSSOVER

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

TRIPLE

Caregivers Investigators Outcome Assessors

Study Groups

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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.

Group Type ACTIVE_COMPARATOR

Unaided Review First, Then AI-Assisted Review

Intervention Type BEHAVIORAL

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.

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.

Group Type ACTIVE_COMPARATOR

AI-Assisted Review First, Then Unaided Review

Intervention Type BEHAVIORAL

Readers first complete Block Y (AI-Assisted) on two assigned subsets: SY1 (up to 40 slides; AI as Double-Check) and SY2 (up to 40 slides; AI as First Look), with the order of Y1 and Y2 randomized. They then complete Block X (Unaided) on subset SX (up to 40 slides). For each reader, SX, SY1, and SY2 are disjoint and stratified by APL status.

Interventions

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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.

Intervention Type BEHAVIORAL

AI-Assisted Review First, Then Unaided Review

Readers first complete Block Y (AI-Assisted) on two assigned subsets: SY1 (up to 40 slides; AI as Double-Check) and SY2 (up to 40 slides; AI as First Look), with the order of Y1 and Y2 randomized. They then complete Block X (Unaided) on subset SX (up to 40 slides). For each reader, SX, SY1, and SY2 are disjoint and stratified by APL status.

Intervention Type BEHAVIORAL

Eligibility Criteria

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Inclusion Criteria

* Wright-Giemsa-stained bone marrow aspirate smears
* Final diagnosis confirmed through molecular testing in conjunction with expert pathology evaluation


* 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

Exclusion Criteria

* Poor-quality or unreadable slides
* Cases used in AI training
Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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National Taiwan University Hospital

OTHER

Sponsor Role collaborator

Far Eastern Memorial Hospital

OTHER

Sponsor Role collaborator

Taipei Veterans General Hospital, Taiwan

OTHER_GOV

Sponsor Role collaborator

Brigham and Women's Hospital

OTHER

Sponsor Role collaborator

Massachusetts General Hospital

OTHER

Sponsor Role collaborator

Harvard Medical School (HMS and HSDM)

OTHER

Sponsor Role lead

Responsible Party

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Kun-Hsing Yu

Associate Professor of Biomedical Informatics

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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Harvard Medical School

Boston, Massachusetts, United States

Site Status

Countries

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United States

Provided Documents

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Document Type: Study Protocol and Statistical Analysis Plan

View Document

Other Identifiers

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IRB23-0403

Identifier Type: -

Identifier Source: org_study_id