RÉSUMÉ
AIM: To establish an intelligent diagnostic model of keratoconus for small-diameter corneas by data mining and analysis of patients' clinical data.METHODS: Diagnostic study. A total of 830 patients(830 eyes)were collected, including 338 male(338 eyes)and 492 female(492 eyes), with an average age of 14-36(23.19±5.71)years. Among them, 731 patients(731 eyes)had undergone corneal refractive surgery at Chongqing Nanping Aier Eye Hospital from January 2020 to March 2022, and 99 patients had a diagnosed keratoconus from January 2015 to March 2022. Corneal diameter ≤11.1 mm was measured by Pentacam in all patients. Two cornea specialists classified patients' data into normal corneas, suspect keratoconus, and keratoconus groups based on the Belin/Ambrósio enhanced ectasia display(BAD)system in Pentacam. The data of 665 patients were randomly selected as the training set and the other 165 patients as the validation set by computer random sampling method. Seven parametric corneal features were extracted by convolutional neural networks(CNN), and the models were built by Residual Network(ResNet), Vision Transformer(ViT), and CNN+Transformer, respectively. The diagnostic accuracy of models was verified by cross-entropy loss and cross-validation method. In addition, sensitivity and specificity were evaluated using receiver operating characteristic curve.RESULTS: The accuracy of ResNet, ViT, and CNN+Transfermer for the diagnosis of normal cornea and suspect keratoconus was 85.57%, 86.11%, and 86.54% respectively, and the area under the receiver operating characteristic curve(AUC)was 0.823, 0.830 and 0.842 respectively. The accuracy of models for the diagnosis of suspect keratoconus and keratoconus was 97.22%, 95.83%, and 98.61%, respectively, and the AUC was 0.951, 0.939, and 0.988 respectively.CONCLUSION: For corneas ≤11.1 mm in diameter, the data model established by CNN+Transformer has a high accuracy rate for classifying keratoconus, which provides real and effective guidance for early screening.
RÉSUMÉ
To research the differences and correlation between Scutellaria baicalensis about phenotypic traits of different strains, 10 aboveground traits and 6 root traits of S. baicalensis in two-year-transplanted plants from 14 different strains were compared respectively, and the SPSS 17.0 statistical software was used for data analysis. It showed that phenotypic traits variation of different S. baicalensis strains was rich and the F value ranged from 3.169 to 71.58. The difference was significant between each other and germplasm 15 performs the most outstanding characters. Correlation analysis showed that there existed a significant correlation between the characters except for lateral root number, root diameter and length. The correlation coefficient between the fresh weight of root and the reed head diameter was up to 0.877. Principal component analysis showed that the average of overall yield per plant and root diameter could be used as the comprehensive reference index for germplasm evaluation. The differences and correlations in phenotypic traits of different S. baicalensis strains, provide theoretical basis for distinguishing germplasm and breeding good varieties of S. baicalensis.