1. Sensitivity and Specificity of AI Guided diagnostics in Hematology (Time Frame - 08-01-2020 until 07-31-2021): 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 outcome:
1. comparison of clinical consequences (Time Frame - 08-01-2020 until 07-31-2021): We will compare the clinical recommendation obtained after routine gold-standard diagnostics and after AI-guided categorization of all samples enrolled in this study
2. predictive diagnostic value (Time Frame - 08-01-2020 until 07-31-2021): We will assess the predictive value of unsupervised categorization and diagnosis in comparison to gold-standard routine testing.
3. turn-around-time (Time Frame - 08-01-2020 until 07-31-2021): We will measure the turn-around-time of gold-standard diagnostics in comparison to AI-guided diagnosis.
4. enumerate entity-specific benchmarks (e.g., blast count in leukemia) count) (Time Frame - 08-01-2020 until 07-31-2021): We will assess secondary disease specific values determined by AI/DNN based unsupervised diagnosis versus routine testing.
Automated AI-Guided Diagnosis of Hematological Malignancies: In BELUGA, we want to investigate whether the automated analysis of blood (from peripheral blood and bone marrow aspirates) smears and flow-cytometry-based analyses can provide a benefit for diagnostic quality and, ultimately, patient care.
Quelle: ClinicalTrials.gov
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"Better Leukemia Diagnostics Through AI (BELUGA)"
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