Clinical trial · Observational
Better Leukemia Diagnostics Through AI (BELUGA)
A Case-Control Study To Determine The Suitability Of Artificial Intelligence For Leukemia Diagnostics
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
To the best of our knowledge, BELUGA will be the first prospective trial investigating the usefulness of deep learning-based hematologic diagnostic algorithms. Taking advantage of an unprecedented collection of diagnostic samples consisting of flow cytometry datapoints and digitalized blood-smears, categorization of yet undiagnosed patient samples will prospectively be compared to current state-of-the-art diagnosis at the Munich Leukemia Laboratory (hereafter MLL). In total, a collection of 25,000 digitalized blood smears and 25,000 flow cytometry datapoints will be prospectively used to train an AI-based deep neuronal network for correct categorization. Subsequently, the superiority will be challenged for the primary endpoints: sensitivity and specificity of diagnosis, most probable diagnosis, and time to diagnose. The secondary endpoints will compare the consequences regarding further diagnostic work-up and, thus, clinical decision making between routine diagnosis and AI guided diagnostics. BELUGA will set the stage for the introduction of AI-based hematologic diagnostics in a real-world setting.
Conditions
Conditions (5)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Blood Cancer | Liquid Tumor | ALIAS | 0.90 |
| Hematologic Malignancy | Hematopoietic and Lymphoid Cell Neoplasm | ALIAS | 0.90 |
| Leukemia | Leukemia | ONTOLOGY_EXACT | 0.90 |
| Lymphoma | Lymphoma | ONTOLOGY_EXACT | 0.90 |
| Minimal Residual Disease | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Automated AI-Guided Diagnosis of Hematological Malignancies | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (1)
- measure
- Sensitivity and Specificity of AI Guided diagnostics in Hematology
- timeFrame
- 08-01-2020 until 07-31-2021
- description
- As a primary endpoint, we will examine the ability of DNN to classify disorders according to (after initial assessment disease/healthy) to the gold-standard diagnosis. The gold-standard diagnosis is defined as an integrated diagnosis, including cytomorphology, flow cytometry, cytogenetics, FISH, and molecular genetics. DNN will independently provide a bi-directional (probabilistic) diagnosis, with the most probable diagnosis. The primary analysis will include a direct comparison between the human cytomorphological examination and the pattern recognition software. Secondly, this result will be provided to downstream diagnostic departments to assess phenotypic diagnosis's usefulness for genetic characterization. We hypothesize that the turn-around time will be significantly enhanced, further providing quality at sooner timepoint.
Secondary outcomes (4)
- measure
- comparison of clinical consequences
- timeFrame
- 08-01-2020 until 07-31-2021
- description
- We will compare the clinical recommendation obtained after routine gold-standard diagnostics and after AI-guided categorization of all samples enrolled in this study
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: * Patients having been diagnosed with a suspected hematological disorder * The suspected diagnoses constitute a primary diagnosis * Only samples of patients min.18 years of age will be used * Samples must suffice quality attributes which are denoted in "Exclusion Criteria" Exclusion Criteria: * The sample is not fit for state-of-the-art diagnosis or fails initial quality control. For quality insurance, we will exclude samples in heparin- instead of EDTA. Samples with damage due to atmospheric reasons (freeze-thaw damage or elevated temperature) will be excluded. * Samples with too scarce material jeopardizing routine gold-standard diagnosis will be excluded. * Bone marrow aspirates without sufficient material to assess malignant or healthy hematopoiesis.
References
Publications (1)
- RESULTZhao M, Mallesh N, Hollein A, Schabath R, Haferlach C, Haferlach T, Elsner F, Luling H, Krawitz P, Kern W. Hematologist-Level Classification of Mature B-Cell Neoplasm Using Deep Learning on Multiparameter Flow Cytometry Data. Cytometry A. 2020 Oct;97(10):1073-1080. doi: 10.1002/cyto.a.24159. Epub 2020 Jun 9. PMID 32519455