Uncovering the Limits of Detection of Artificial Intelligence using Synthetic Lesions in Positron Emission Tomography
Bibliographic record
Abstract
The use of artificial intelligence (AI) for detection of lesions has been proposed to aid clinicians in detection tasks.However, before AI can be used clinically, the limits of detection must be studied to characterize AI performance.In this work, a library was constructed containing well-characterized spherical synthetic lesions in real positron emission tomography (PET) and x-ray computed tomography (CT) patient data, which had been previously reported free of lesions by expert physicians.These lesions were manually defined and automatically synthesized using the Lesion Synthesis Toolbox (LST).This library was used to study two FDG PET lesion-detection AI algorithms by their ability to detect lesions by size and intensity metrics, including lesion intensity, contrast, and contrast-to-noise ratio.The work demonstrates the utility of synthetic lesions for characterizing the limits of detection of AI and necessary tools available to other researchers.Q. de Bourbon, 2023 vi 4.3.4False positives .............
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".