Diffusion tensor imaging of optic neuropathies: a narrative review
Bibliographic record
Abstract
Background and Objective: Diffusion tensor imaging (DTI) has been implemented in a breadth of scientific investigations of optic neuropathies, though it has yet to be fully adopted for diagnosis or prognosis. This is potentially due to a lack of standardization and weak replication of results. The aim of this investigation was to review DTI results from studies specific to three distinct optic neuropathies in order to probe its current clinical utility. Methods: , 2023. Four distinct DTI metrics are considered: fractional anisotropy (FA), along with mean diffusivity (MD, axial diffusivity (AD), and radial diffusivity (RD). Results from within-group, between-group, and correlational studies were thoroughly assessed. Key Content and Findings: POAG studies most consistently report a decrease in FA, especially in the optic radiations, followed in prevalence by an increase in RD and then MD, whilst AD yields conflicting results between studies. It is notable that there is not an equal distribution of investigated DTI metrics, with FA utilized the most, followed by MD, RD, and AD. Studies of ON are similar in that the most consistent findings are specific to FA, RD, and MD. These results are specific to the optic nerve and radiation since only one study measured the intermediary regions. More studies are needed to assess the effect that ON has on the tracts of the visual system. Finally, only three studies assessing DTI of TON have been performed to date, displaying low to moderate replicability of results. To improve the level of agreement between studies assessing each optic neuropathy, an increased level of standardization is recommended. Conclusions: Both POAG and ON studies have yielded some prevalent DTI findings, both for contrast and correlation-based assessments. Although the clinical need is high for TON, considering the limitations of the current diagnostic tools, too few studies exist to make confident conclusions. Future use of standardized and longitudinal DTI, along with the foreseen methodological and technical improvements, is warranted to effectively study optic neuropathies.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".