Performance evaluation of a dynamic telepathology system (Panoptiq<sup>™</sup>) in the morphologic assessment of peripheral blood film abnormalities
Bibliographic record
Abstract
INTRODUCTION: The study evaluated the performance of a dynamic imaging telepathology system (Panoptiq(™) ) as a diagnostic aid to the identification of peripheral blood film (PBF) abnormalities. METHODS: The study assumed a laboratory personnel working in a clinical laboratory were operating the telepathology system to seek diagnostic opinion from an external consulting hematopathologist. The study examined 100 blood films, encompassing 23 different hematological diseases, reactive or normal cases. RESULTS: The study revealed that with real-time image transmission in live scanning mode of operation, the telepathology system was able to aid reviewers in achieving excellent accuracy, that is correct interpretation of morphologic abnormalities obtained in 83/84 of the hematologic diseases and 12/12 of the reactive/normal conditions (Sensitivity: 0.99; Specificity: 1.00). In contrast, when only saved static images in digital capture mode of operation were reviewed remotely, interpretative omissions occurred in 8/84 of the hematologic diseases and 0/12 of the reactive/normal conditions (Sensitivity: 0.91; Specificity: 1.00). It is hypothesized that real-time operator-reviewer communication during live scanning played an important role in the identification of key morphologic abnormalities for review. CONCLUSION: Our study showed the Panoptiq system can be adopted reliably as a dynamic telepathology tool in aiding community laboratories in the triage of PBF cases for external diagnostic consultation.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".