Artificial intelligence approaches to the volumetric quantification of glycogen granules in EM images of human tissue
Bibliographic record
Abstract
Skeletal muscle, the major processor of dietary glucose, stores it in myriad glycogen granules. Their numbers vary with cellular location and physiological and pathophysiological states. AI models were developed to derive granular glycogen content from electron-microscopic images of human muscle. Two UNet-type semantic segmentation models were built: "Locations" classified pixels as belonging to different regions in the cell; "Granules" identified pixels within granules. From their joint output, a pixel fraction pf was calculated for images from patients positive (MHS) or negative (MHN) to a test for malignant hyperthermia susceptibility. pf was used to derive vf, the volume fraction occupied by granules. The relationship vf (pf) was derived from a simulation of volumes ("baskets") containing virtual granules at realistic concentrations. The simulated granules had diameters matching the real ones, which were measured by adapting a utility devised for calcium sparks. Applying this relationship to the pf measured in images, vf was calculated for every region and patient, and from them a glycogen concentration. The intermyofibrillar spaces and the sarcomeric I band had the highest granular content. The measured glycogen concentration was low enough to allow for a substantial presence of non-granular glycogen. The MHS samples had an approximately threefold lower concentration (significant in a hierarchical test), consistent with earlier evidence of diminished glucose processing in MHS. The AI models and the approach to infer three-dimensional magnitudes from two-dimensional images should be adaptable to other tasks on a variety of images from patients and animal models and different disease conditions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".