The Value of Diffusion Kurtosis Imaging In Detecting Delayed Brain Development of Premature Infants
Bibliographic record
Abstract
Abstract Objective Preterm infants are at high risk of adverse neurodevelopmental outcome. Our aim is to explore the value of diffusion kurtosis imaging (DKI) in diagnosing brain developmental disorders in premature infants.Materials and Methods A total of 52 subjects were included in this study, including 26 premature infants as the preterm group, and 26 full-term infants as the control group. Routine magnetic resonance imaging and DKI examination were performed. Mean kurtosis (MK), radial kurtosis (RK), fractional anisotropy (FA), mean diffusivity (MD) values were measured in the brain regions including posterior limbs of the internal capsule (PLIC); anterior limb of internal capsule (ALIC); parietal white matter (PWM); frontal white matter (FWM); thalamus (TH); caudate nucleus (CN); genu of the corpus callosum (GCC). The X2, t test, Spearman’s correlation analysis and receiver operating characteristic curve (ROC)were used for data analyses.Results In the premature infant group, the MK and RK values of PLIA, ALIC, and PWM were lower than those in the control group (P<0.05). The FA values of PWM, FWM and TH were also lower than those of the control group (P<0.05). The AUCs of MK in PLIC and ALIC, MD in PWM, and FA in FWM were 0.813, 0.802, 0.842 and 0.867 (P<0.05). In thalamus and caudate nucleus, the correlations between MK, RK values and PMA were higher than those between FA, MD values and PMA.Conclusions DKI can be used as an effective tool in detecting brain developmental disorders in premature infants.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".