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

NCT07203885 | Completed

• 1 other identifier: IRB23-0403
• Study Type: interventional
• Target: 10
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

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

Keywords

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

Outcome Measures

Primary Outcomes (1)

• Diagnostic performance of APL detection

  Performance of clinicians (unaided and AI-assisted) in detecting APL, measured in accuracy, sensitivity, specificity, positive predictive value, and negative predictive value.

  Periprocedural (at the time of slide review)

Secondary Outcomes (6)

• 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)

Study Arms (2)

Unaided Review First, Then AI-Assisted Review

ACTIVE COMPARATOR

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.

Behavioral: Unaided Review First, Then AI-Assisted Review

AI-Assisted Review First, Then Unaided Review

ACTIVE COMPARATOR

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.

Behavioral: AI-Assisted Review First, Then Unaided Review

Interventions

Unaided Review First, Then AI-Assisted Review 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.

Unaided Review First, Then AI-Assisted Review

AI-Assisted Review First, Then Unaided Review 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.

AI-Assisted Review First, Then Unaided Review

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

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

You may not qualify if:

• Poor-quality or unreadable slides
• Cases used in AI training
• 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

Sponsors & Collaborators

• Harvard Medical School (HMS and HSDM): lead
• National Taiwan University Hospital: collaborator
• Far Eastern Memorial Hospital: collaborator
• Taipei Veterans General Hospital, Taiwan: collaborator
• Brigham and Women's Hospital: collaborator
• Massachusetts General Hospital: collaborator

Study Sites (1)

Harvard Medical School

Boston, Massachusetts, 02115, United States

Location

MeSH Terms

Conditions

Leukemia, Promyelocytic, Acute; Leukemia, Myeloid, Acute

Condition Hierarchy (Ancestors)

Leukemia, Myeloid; Leukemia; Neoplasms by Histologic Type; Neoplasms; Hematologic Diseases; Hemic and Lymphatic Diseases

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: TRIPLE
• Who Masked: CARE PROVIDER, INVESTIGATOR, OUTCOMES ASSESSOR
• Purpose: DIAGNOSTIC
• Intervention Model: CROSSOVER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Professor of Biomedical Informatics

Study Record Dates

• First Submitted: September 8, 2025
• First Posted: October 2, 2025
• Study Start: September 9, 2025
• Primary Completion: October 10, 2025
• Study Completion: October 10, 2025
• Last Updated: October 31, 2025

Record last verified: 2025-10

Data Sharing

• IPD Sharing: Will not share

Locations

US (1)