Search

Purpose: Assess the comparative accuracy and reliability of postoperative toric intraocular lens (TIOL) alignment measurement methods: Casia2 and Adobe Photoshop with digital slit lamp images (PS method). Methods: In a study of 41 subjects with 58 eyes postimplantation of TIOL, we independently measured TIOL alignment with Casia2 and PS methods. The intraclass correlation coefficient (ICC1,1) was employed to assess the repeatability of the Casia2 method. While ICC2,1 for absolute agreement and Bland–Altman analysis were utilized to determine the interdevice agreement between the two methods, the regression analysis was conducted to identify any proportional bias. Results: Casia2 demonstrated excellent intradevice repeatability with an ICC1,1 of 0.998. The absolute agreement between Casia2 and PS was very high with an ICC2,1 of 0.999. The average discrepancy between the two measurement methods was −0.828°, with a 95% confidence interval (CI) ranging from −1.623° to −0.032°. The 95% limits of agreement (LoA) were between −6.761° and 5.105°, indicating a strong concordance in TIOL alignment measurements. Casia2 was capable of accurately measuring the TIOL axis alignment under conditions of pupil diameters (PDs) of 4 mm or greater. Conclusion: Casia2 and PS demonstrated significant concordance in measuring postoperative TIOL alignment, with Casia2 offering a more straightforward and accessible alternative, particularly beneficial for patients with suboptimal pupil dilation. [ABSTRACT FROM AUTHOR]

Based on the characteristic parameters, feature selection and fiber clustering analysis are studied for the yarn of polyester/cotton, comparison among the characteristic parameters is discussed and finally the characteristic of fiber shaped degree is adopted to classify polyester/cotton. Results indicate that fiber shaped degree can be used to distinguish the blend yarn of polyester/cotton accurately. Compared with Hierarchical clustering analysis method, Fuzzy C-means cluster algorithm represents a significant advance and can be applied to fiber recognition more effectively. [ABSTRACT FROM AUTHOR]