Segmentation Errors in the Measurement of Volumetric Parameters by Swept-Source Anterior Segment Optical Coherence Tomography

Purpose: To investigate the error rate of segmentation in the automatic measurement of anterior chamber volume (ACV) and iris volume (IV) by swept-source anterior segment optical coherence tomography (SS-ASOCT) in narrow-angle and wide-angle eyes.Methods: In this study, fifty eyes from 25 narrow-angle subjects and fifty eyes from 25 wide-angle subjects were enrolled. SS-ASOCT examinations were performed and each SS-ASOCT scan was reviewed, and segmentation errors in the automatic measurement of ACV and IV were classified and manually corrected. Error rates were compared between the narrow-angle and the wide-angle groups, and ACV and IV before and after manual correction were compared.Results: A total of 12,800 SS-ASOCT scans were reviewed. Segmentation error rates of angle recess, iris root, posterior surface of the iris, pupil margin, and anterior surface of the lens were 84.06, 93.30, 13.15, 59.21, and 25.27%, respectively. Segmentation errors of angle recess, iris root, posterior surface of the iris, and pupil margin occurred more frequently in narrow-angle eyes, while more segmentation errors of the anterior surface of the lens were found in wide-angle eyes (all P < 0.001). ACV decreased and IV increased significantly after manual correction of segmentation errors in both groups (all P < 0.01).Conclusion: Segmentation errors were prevalent in the volumetric measurement by SS-ASOCT, particularly in narrow-angle eyes, leading to mismeasurement of ACV and IV.

REMOTE AUTOMATIC SYSTEMS FOR MEASUREMENT OF PATIENTS’ TEMPERATURES AND ARTERIAL PRESSURE IN MEDICAL INSTITUTIONS

This paper presents systems for remote automatic measurement of temperature and blood pressure in patients of medical organizations based on classical methods and devices used in medical institutions.

Improvement of Automatic Measurement Evaluation System for Subway Structures by Adjacent Excavation

This study evaluated the structural stability of subway structures based on adjacent excavations by comparing automatically measured and numerically analyzed data. The reliability of the automated measurement methodology was evaluated by first applying probability statistical analysis to the measured results and then comparing these results with the numerically analyzed results. An improvement in the calculation method evaluation system, including the method of processing and analysis of the automatically measured data of subway structures through the average value of probability density, was proposed. As a result of the field measurement and numerical analysis, the measured results of tunnel displacement and track deformation exhibited some differences. However, it was determined that the construction stage and location where the maximum values of the tunnel displacement and track deformation occurred had similarities.

Artificial intelligence-based automatic assessment of lower limb torsion on MRI

Abnormal torsion of the lower limbs may adversely affect joint health. This study developed and validated a deep learning-based method for automatic measurement of femoral and tibial torsion on MRI. Axial T2-weighted sequences acquired of the hips, knees, and ankles of 93 patients (mean age, 13 ± 5 years; 52 males) were included and allocated to training (n = 60), validation (n = 9), and test sets (n = 24). A U-net convolutional neural network was trained to segment both femur and tibia, identify osseous anatomic landmarks, define pertinent reference lines, and quantify femoral and tibial torsion. Manual measurements by two radiologists provided the reference standard. Inter-reader comparisons were performed using repeated-measures ANOVA, Pearson’s r, and the intraclass correlation coefficient (ICC). Mean Sørensen-Dice coefficients for segmentation accuracy ranged between 0.89 and 0.93 and erroneous segmentations were scarce. Ranges of torsion as measured by both readers and the algorithm on the same axial image were 15.8°–18.0° (femur) and 33.9°–35.2° (tibia). Correlation coefficients (ranges, .968 ≤ r ≤ .984 [femur]; .867 ≤ r ≤ .904 [tibia]) and ICCs (ranges, .963 ≤ ICC ≤ .974 [femur]; .867 ≤ ICC ≤ .894 [tibia]) indicated excellent inter-reader agreement. Algorithm-based analysis was faster than manual analysis (7 vs 207 vs 230 s, p < .001). In conclusion, fully automatic measurement of torsional alignment is accurate, reliable, and sufficiently fast for clinical workflows.