AI-Assisted Review for Acute Promyelocytic Leukemia
(LEAP Trial)
What You Need to Know Before You Apply
What is the purpose of this trial?
The trial aims to determine if artificial intelligence (AI) can assist doctors in diagnosing acute promyelocytic leukemia (APL), a rare blood cancer, more quickly and accurately. Doctors will review bone marrow samples in three ways: independently, with AI as a double-check, and with AI as the initial reviewer. The trial compares the impact of these methods on accuracy, speed, and confidence. This trial suits board-certified or eligible pathologists and hematologists who regularly diagnose blood disorders and are open to incorporating AI into their evaluations. As an unphased trial, it offers a unique opportunity to contribute to innovative research that could transform diagnostic practices.
Do I need to stop my current medications for this trial?
The trial information does not specify whether you need to stop taking your current medications.
What prior data suggests that AI-assisted review is safe for diagnosing acute promyelocytic leukemia?
Research has shown that AI can help doctors diagnose acute promyelocytic leukemia (APL), a rare type of blood cancer, more quickly and accurately. AI could thus serve as a valuable tool in identifying this cancer.
Regarding safety, using AI in this manner poses no direct risks. The AI does not physically interact with patients; it assists doctors by analyzing bone marrow samples. Therefore, it is generally safe, as the AI supports the diagnosis process rather than serving as a treatment itself.12345Why are researchers excited about this trial?
Researchers are excited about the AI-assisted review for acute promyelocytic leukemia (APL) because it offers a novel approach to diagnosing this condition. Unlike traditional methods that rely solely on pathologists’ expertise, this AI system acts as both a double-check and a first look, potentially increasing accuracy and speed. By integrating AI, the process could become more efficient, allowing for quicker and possibly more precise diagnoses, which is especially crucial in critical conditions like APL. This technology could transform how pathologists work, ensuring they have cutting-edge tools to support their decision-making.
What evidence suggests that AI-Assisted Review is effective for diagnosing acute promyelocytic leukemia?
Research has shown that AI can be a helpful tool in diagnosing acute promyelocytic leukemia (APL), a rare type of blood cancer. In this trial, participants will undergo two different review processes: one with AI assistance first, followed by an unaided review, and another with the unaided review first, followed by AI assistance. Studies have found that AI systems like MC-100i effectively spot abnormal promyelocytes, a type of white blood cell, in blood samples. This capability helps doctors make faster and more accurate diagnoses. With AI's help, screening and identifying APL can occur more quickly, which is crucial for timely treatment. Overall, AI support in diagnosis can improve accuracy and boost confidence in medical evaluations.13467
Are You a Good Fit for This Trial?
This trial is for doctors who diagnose acute promyelocytic leukemia (APL), a rare blood cancer. It's designed to see if AI can improve the speed and accuracy of APL diagnosis when reviewing bone marrow samples.Inclusion Criteria
Exclusion Criteria
Timeline for a Trial Participant
Screening
Participants are screened for eligibility to participate in the trial
Diagnostic Review
Clinicians review bone marrow samples under different conditions: Unaided Review, AI as Double-Check, and AI as First Look
Follow-up
Participants are monitored for safety and effectiveness after diagnostic review
What Are the Treatments Tested in This Trial?
Interventions
- AI-Assisted Review
Trial Overview
The study tests three diagnostic approaches: unaided review by doctors, AI assistance after initial doctor evaluation, and AI analysis before the doctor reviews bone marrow samples. Doctors will be randomly assigned to these methods in different sequences.
How Is the Trial Designed?
2
Treatment groups
Active Control
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.
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.
Find a Clinic Near You
Who Is Running the Clinical Trial?
Harvard Medical School (HMS and HSDM)
Lead Sponsor
Massachusetts General Hospital
Collaborator
Far Eastern Memorial Hospital
Collaborator
Taipei Veterans General Hospital, Taiwan
Collaborator
Brigham and Women's Hospital
Collaborator
National Taiwan University Hospital
Collaborator
Citations
Study Details | NCT07203885 | AI-Assisted Acute Myeloid ...
This study aims to evaluate the effect of artificial intelligence (AI) assistance on clinicians' diagnostic performance in detecting acute promyelocytic ...
Artificial intelligence-assisted early screening of acute ...
Our findings indicate that MC-100i is an effective tool for identifying abnormal promyelocytes in blood smears. MC-100i can be used to assist in ...
AI-Assisted Review for Acute Promyelocytic Leukemia · Info ...
This study will test whether artificial intelligence (AI) can help doctors diagnose a rare blood cancer called acute promyelocytic leukemia ...
Applications of Artificial Intelligence in Acute Promyelocytic ...
AI can emerge as a relevant tool in the evaluation of APL cases and potentially contribute to more rapid screening and identification of this hematological ...
5.
ashpublications.org
ashpublications.org/blood/article/144/12/1257/516241/Clinical-networking-results-in-continuousClinical networking results in continuous improvement of the ...
The establishment of a clinical network led to significant and lasting advancements in the treatment outcomes of APL over a span of 15 years.
Applications of Artificial Intelligence in Acute Promyelocytic ...
AI can emerge as a relevant tool in the evaluation of APL cases and potentially contribute to more rapid screening and identification of this hematological ...
7.
karger.com
karger.com/aha/article/148/5/583/930961/Toward-Clinically-Actionable-Machine-Learning-and?searchresult=1Toward Clinically Actionable Machine Learning and ...
This review highlights the utilization of contemporary AI/ML algorithms via addressing diagnostic challenges, molecular risk stratification ...
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