Deep learning-based analysis of optical imaging for precision medicine applications in smart healthcare
Bibliographic record
Abstract
This research presents an innovative framework that integrates optical imaging with deep learning to analyze the therapeutic impact of Rigolazine and Tanshinone in managing preeclampsia. By integrating fluorescence and hyperspectral imaging, this study identified key biomarkers and quantified molecular changes, particularly in oxidative stress and inflammation, to reveal the mechanisms underlying the therapeutic effects of drugs. The deep learning-based model demonstrates exceptional accuracy in processing multimodal biomedical data, validating the therapeutic efficacy of chasteberry glycosides and tanshinone. Furthermore, the study establishes a scalable methodology that can be extended to evaluate other drugs and diseases. With its ability to deliver high-throughput analysis and precise molecular insights, the proposed framework contributes significantly to the advancement of precision medicine. Additionally, this approach holds great potential for applications in public health monitoring and personalized healthcare within smart city ecosystems. Future research will focus on expanding datasets to include diverse pathological conditions and populations, while enhancing the integration of optical imaging and deep learning to improve scalability and analytical efficiency, paving the way for intelligent healthcare innovations.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".