Improving point correspondence in cephalograms by using a two-stage rectified point transform

Human examination and artificial intelligence in cephalometric landmark detection-is AI ready to take over?

OBJECTIVES

To compare the precision of two cephalometric landmark identification methods, namely a computer-assisted human examination software and an artificial intelligence program, based on South African data.

METHODS

This retrospective quantitative cross-sectional analytical study utilized a data set consisting of 409 cephalograms obtained from a South African population. 19 landmarks were identified in each of the 409 cephalograms by the primary researcher using the two programs [(409 cephalograms x 19 landmarks) x 2 methods = 15,542 landmarks)]. Each landmark generated two coordinate values (x, y), making a total of 31,084 landmarks. Euclidean distances between corresponding pairs of observations was calculated. Precision was determined by using the standard deviation and standard error of the mean.

RESULTS

The primary researcher acted as the gold-standard and was calibrated prior to data collection. The inter- and intrareliability tests yielded acceptable results. Variations were present in several landmarks between the two approaches; however, they were statistically insignificant. The computer-assisted examination software was very sensitive to several variables. Several incidental findings were also discovered. Attempts were made to draw valid comparisons and conclusions.

CONCLUSIONS

There was no significant difference between the two programs regarding the precision of landmark detection. The present study provides a basis to: (1) support the use of automatic landmark detection to be within the range of computer-assisted examination software and (2) determine the learning data required to develop AI systems within an African context.

Artificial Intelligence for Detecting Cephalometric Landmarks: A Systematic Review and Meta-analysis

Using computer vision through artificial intelligence (AI) is one of the main technological advances in dentistry. However, the existing literature on the practical application of AI for detecting cephalometric landmarks of orthodontic interest in digital images is heterogeneous, and there is no consensus regarding accuracy and precision. Thus, this review evaluated the use of artificial intelligence for detecting cephalometric landmarks in digital imaging examinations and compared it to manual annotation of landmarks. An electronic search was performed in nine databases to find studies that analyzed the detection of cephalometric landmarks in digital imaging examinations with AI and manual landmarking. Two reviewers selected the studies, extracted the data, and assessed the risk of bias using QUADAS-2. Random-effects meta-analyses determined the agreement and precision of AI compared to manual detection at a 95% confidence interval. The electronic search located 7410 studies, of which 40 were included. Only three studies presented a low risk of bias for all domains evaluated. The meta-analysis showed AI agreement rates of 79% (95% CI: 76-82%, I2 = 99%) and 90% (95% CI: 87-92%, I2 = 99%) for the thresholds of 2 and 3 mm, respectively, with a mean divergence of 2.05 (95% CI: 1.41-2.69, I2 = 10%) compared to manual landmarking. The menton cephalometric landmark showed the lowest divergence between both methods (SMD, 1.17; 95% CI, 0.82; 1.53; I2 = 0%). Based on very low certainty of evidence, the application of AI was promising for automatically detecting cephalometric landmarks, but further studies should focus on testing its strength and validity in different samples.

Protective role of murine norovirus against Pseudomonas aeruginosa acute pneumonia

The murine norovirus (MNV) is a recently discovered mouse pathogen, representing the most common contaminant in laboratory mouse colonies. Nevertheless, the effects of MNV infection on biomedical research are still unclear. We tested the hypothesis that MNV infection could alter immune response in mice with acute lung infection. Here we report that co-infection with MNV increases survival of mice with Pseudomonas aeruginosa acute lung injury and decreases in vivo production of pro-inflammatory cytokines. Our results suggest that MNV infection can deeply modify the parameters studied in conventional models of infection and lead to false conclusions in experimental models.