Automated myeloma cell selection using machine learning and artificial intelligence.
Bibliographic record
Abstract
7565 Background: Precision of sampling is critical to achieve accurate results relevant to diagnostics and prognostics. Sampling errors may compromise the sensitivity and specificity of otherwise credible diagnostics technologies. Our 3- dimensional (3D) telomeres profiling methodology is conducted on individual target cells selected from the patient sample processed using 3D immuno-FISH. In our traditional workflow target cells are manually selected by highly trained personnel based on morphological characteristics and/ or immunophenotypic characteristics, namely cells positive for CD138 and CD56. However, the variability among trained operators required rounds of verification by a second and third operator and an approval by a Hematopathologist or the Lab Director to confirm the selected cells in some cases. Therefore, automated detection of myeloma cells is of paramount importance. Methods: In this study we employed machine learning and artificial intelligence (AI) tools to develop an automated algorithm capable of streamlining target cell (i.e. myeloma) selection and minimize or eliminate operator introduced variability. Training libraries were built using positive and negative cells. Over 5000 cells were used in the algorithm training process. We employed iterative rounds of validation and refinement to achieve a high precision automated tool. AI then identified myeloma cells in an automated fashion and the results of AI identification were confirmed by a Hematopathologist. Results: We report the results of a comparative analysis done on 20 myeloma patients at different stages of the disease including MGUS, smouldering myeloma and active myeloma. Also, the sample type included bone marrow smears, purified mono-nucleated plasma cells and bone marrow biopsies. 50 verified manually selected target cells and 50 target cells selected by the automated cell selection tool, approved by a skilled operator, from the same sample of each patient were analyzed using the TeloView platform. The TeloView platform quantifies 6 telomeric molecular and structural parameters. We conducted statistical analysis to calculate Coefficient of Variation (CV) between the results of each patient. We set the acceptable variability to < 15%. We achieved CVs of less than 10% among all patients included in the analysis. Conclusions: The automated cell selection tool streamlined this critical sampling process in our workflow minimizing operator-introduced variability, reducing the processing time to a fraction of the time required for manual cell selection and verification which enables high throughput for the 3D telomeres profiling while maintaining precision.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".