RESUMO
Developmental dysplasia of the hip (DDH) is a common skeletal malformation in children and the prominent cause of hip osteoarthritis and lower limb disability. The therapeutic difficulty and effect of DDH are closely related to an early and proper diagnosis. Hip ultrasonography and anteroposterior pelvic radiography are preferred depending on the presence of the secondary ossification center of the femoral head. Conventional diagnostic methods primarily relied on manual measurements and empirical judgments by clinicians, which were laborious and generally lacked reliability. The effective integration of medical imaging and artificial intelligence algorithms is expected to improve the diagnosis of pediatric DDH and enhance the efficiency of clinical diagnosis and treatment. Segmentation algorithms based on the extraction of local geometric features, 3D map search-based segmentation algorithms, and deep learning networks were utilized to assist in analyzing hip ultrasound images, calculating key dysplasia indicators, and diagnosing DDH in infants under 4-6 months. Computer-aided techniques, such as bone edge detection and template matching algorithms, deep transfer learning algorithms, and local-global feature mining convolutional neural networks were used to automatically identify bony landmarks on pelvic radiographs for measuring hip parameters and evaluating DDH in children over 4-6 months. However, there were several crucial problems in the clinical application of the artificial intelligence model for the auxiliary diagnosis of DDH due to technical limitations and insufficient understanding of researchers. This paper aims to review the progress of application in the medical artificial intelligence technology for the clinical auxiliary diagnosis of DDH. The author also provides references for future research for truly intelligent diagnostic tools.
RESUMO
OBJECTIVE@#To set up near-field microwave detection system, to evaluate the accuracy of measuring water and to compare the effects of different methods on drying root canal.@*METHODS@#Known and quantitative distilled water in Eppendorf tube and root canal was detected by near-field microwave detection system which was set up first. The microwave reflection coefficient was recorded so as to evaluate the accuracy of the measuring water. 12 single-rooted mandibular premolar teeth with apical matureness and a curvature less than 10° were decoronated, leaving roots 14 mm in length. After instrumentation to F3, the root canals were air-dried and injected with 10 μL distilled water each. Then all the root canals were assigned to 4 groups (n=12) according to different methods on root canal drying. Near-field microwave detection system was used to detect the change of water after air-drying and root canal drying so as to evaluate the effects of different root canal drying methods: (1) negative control group, the canals were injected with 10 μL distilled water and no root canal drying method was performed; (2) paper points drying experimental group, four #60 paper points were used to blot-dry the middle and upper part of the canal, followed by #40 paper points blot-drying the apical area. The above step was repeated with the tip of paper point that appeared with no water under microscope as finished; (3) 95% (volume fraction) ethanol drying experimental group, after the distilled water was removed with paper points (similar to paper points experimental group), 10 μL 95% ethanol was injected into the canal and was left in place for 10 s, then paper points were used to dry the canal; (4) 70% (volume fraction) isopropyl alcohol drying experimental group, after the distilled water was removed with paper points (similar to paper points experimental group), 10 μL 70% isopropyl alcohol was injected into the canal and was left in place for 10 s, then paper points were used to dry the canal. The linear correlation analysis was used to test the accuracy of the measuring water by near-field microwave detection method and physical measurement. The Pearson correlation coefficient was calculated based on the data. The effects of different methods on drying root canal were analyzed by Chi square test.@*RESULTS@#The R2 for the microwave reflection coefficient and the physical measurement of the water in Eppendorf tube and root canal were 0.991 and 0.970. The microwave reflection coefficient in all the experimental groups showed significant difference (P<0.05). After drying with paper points, the residual water was the most, and the microwave reflection coefficient was the highest: 0.023. While after 70% isopropyl alcohol drying root canal, the residual water was the least with the microwave reflection coefficient 0.006, showing that the effect of drying was the best.@*CONCLUSION@#Near-field microwave detection system could detect the change of water in root canal. The effect of root canal drying by 70% isopropyl alcohol was superior to 95% ethanol and paper points.