C.2 Muscular MRI pattern recognition for muscular dystrophies: the era of artificial intelligence beyond a systematic review
Bibliographic record
Abstract
Background: Genetic neuromuscular diseases (NMD) are a heterogeneous group of disorders comprised hundreds of genes. Despite the advanced genetic testing modalities, about 40 % of patients with NMD do not have a diagnosis. Muscle MRI has been proven as a useful tool to orientate the genetic testing by looking at the muscle involvement severity pattern. Moreover, muscle MRI patterns can be specific and informative for muscular dystrophies and yet can be characteristic and diagnostic. Methods: Systematic review was conducted to review muscle MRI patters for all Limb Girdle Muscle Dystrophies (LGMD). Then, we applied artificial intelligence (AI) on muscle MRI patterns for LGMDs and other NMDs using open database containing muscle MRIs Mercuri scores from 950 individuals. Results: AI and machine learning were applied on 10 types of NMD muscle MRI Mercuri scores that represented muscle involvement based on the degree of fatty infiltration. Different models were generated, the one with highest accuracy was used. When tested on new patients, it achieved a 90% accuracy. Subsequently, was turned into a mobile application. Conclusions: Muscle MRI is a valuable tool to help in NMD diagnosis. Specific muscle involvement pattern can be predictive. Besides, AI facilitates the interpretation and comprehension of muscle imagining in NMD.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".