Measurements of attenuation coefficient for evaluating the hardness of a cataract lens by a high-frequency ultrasonic needle transducer
Bibliographic record
Abstract
A cataract is a clouding of the lens in the eye that affects vision. Phacoemulsification is the mostly common surgical method for treating cataracts, and determining that the optimal phacoemulsification energy is dependent on measuring the hardness of the lens. This study explored the use of an ultrasound needle transducer for invasive measurements of ultrasound attenuation coefficient to evaluate the hardness of the cataract lens. A 47 MHz high-frequency needle transducer with a diameter of 0.9 mm was fabricated by a polarized PMN-33%PT single crystal in the present study. The attenuation coefficients at different stages of an artificial porcine cataract lens were measured using the spectral shift approach. The hardness of the cataract lens was also evaluated by mechanical measurement of its elastic properties. The results demonstrated that the ultrasonic attenuation coefficient was increased from 0.048 +/- 0.02 to 0.520 +/- 0.06 dB mm(-1) MHz(-1) corresponding to an increase in Young's modulus from 6 +/- 0.4 to 96 +/- 6.2 kPa as the cataract further developed. In order to evaluate the feasibility of combining needle transducer and phacoemulsification probe for real-time measurement during cataract surgery, the needle transducer was mounted on the phacoemulsification probe for a vibration test. The results indicated that there was no apparent damage to the tip of the needle transducer and the pulse-echo test showed that a good performance in sensitivity was maintained after the vibration test.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".