Can we use artificial intelligence tools for automatic analysis of bone marrow samples?

Evaluation of a digital ecosystem leveraging mobile technology and artificial intelligence for digitalization and remote analysis of bone marrow samples

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

ISRCTN

Registry ID

ISRCTN10382623

Enrollment

150

Registered

2020-12-11

Start date

2021-05-17

Completion date

Unknown

Last updated

2022-04-25

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Training of convolutional neural network algorithms for identification and counting of cellular lineages and specific cell types of bone marrow Not Applicable

Interventions

This is a one-centre, observational study to evaluate benefits of digitalization of collected BM samples in Hospital Universitario 12 Octubre from patients with suspected hematological disease. Genera

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: Patients: 1. Suspected hematological disease 2. Signed informed consent Bone marrow samples: 1. Good quality BMA sample (with proper staining and lump to provide sufficient quality and quantity) Professionals/experts: 1. Sanitary professionals of the National Health System (Doctors, Cytologists) working at Hematology Department of the Hospital Universitario 12 Octubre with microscopy experience on hematological diseases

Exclusion criteria

Exclusion criteria: Patients: 1. Individuals unwilling to participate in the study 2. Unspecified reasons that, in the opinion of the investigator or sponsor, make the subject unsuitable for enrollment Bone marrow samples: 1. BMA samples that do not have a good quality stain 2. BMA samples with insufficient lump

Design outcomes

Primary

Measure	Time frame
1. Number of samples analysed by web platform (TeleSpot) and analysis time per sample 2. Professionals' satisfaction measured with the new system measured by a usability report based on the results from a system usability scale (SUS) and AdaptaSpot Usability Questionnaire evaluating the remote analysis process. The SUS and the product questionnaires are completed every three months during the length of the study	—

Measure

Time frame

1. Number of samples analysed by web platform (TeleSpot) and analysis time per sample 2. Professionals' satisfaction measured with the new system measured by a usability report based on the results from a system usability scale (SUS) and AdaptaSpot Usability Questionnaire evaluating the remote analysis process. The SUS and the product questionnaires are completed every three months during the length of the study

—

Secondary

Measure	Time frame
1. Number of digitized bone marrow aspirate images correctly marked and tagged 2. Accuracy of the AI algorithm developed and the % of agreement among experts and AI algorithm. Cell-type classification performance will be tested by assessing the prediction quality of the algorithm in the validation set compared to the ground truth annotated by the specialist during the labelling phase.	—

Countries

Spain

Contacts

Public ContactElisa Álamo García-Donas

elisa@spotlab.org+34 675574488

Outcome results

None listed

Source: ISRCTN (via WHO ICTRP) · Data processed: Feb 4, 2026