The uncovered biases and errors in clinical determination of bone age by using deep learning models.

OBJECTIVES: To evaluate AI biases and errors in estimating bone age (BA) by comparing AI and radiologists' clinical determinations of BA. METHODS: We established three deep learning models from a Chinese private dataset (CHNm), an American public dataset (USAm), and a joint dataset combining the above two datasets (JOIm). The test data CHNt (n = 1246) were labeled by ten senior pediatric radiologists. The effects of data site differences, interpretation bias, and interobserver variability on BA assessment were evaluated. The differences between the AI models' and radiologists' clinical determinations of BA (normal, advanced, and delayed BA groups by using the Brush data) were evaluated by the chi-square test and Kappa values. The heatmaps of CHNm-CHNt were generated by using Grad-CAM. RESULTS: We obtained an MAD value of 0.42 years on CHNm-CHNt; this result indicated an appropriate accuracy for the whole group but did not indicate an accurate estimation of individual BA because with a kappa value of 0.714, the agreement between AI and human clinical determinations of BA was significantly different. The features of the heatmaps were not fully consistent with the human vision on the X-ray films. Variable performance in BA estimation by different AI models and the disagreement between AI and radiologists' clinical determinations of BA may be caused by data biases, including patients' sex and age, institutions, and radiologists. CONCLUSIONS: The deep learning models outperform external validation in predicting BA on both internal and joint datasets. However, the biases and errors in the models' clinical determinations of child development should be carefully considered. KEY POINTS: • With a kappa value of 0.714, clinical determinations of bone age by using AI did not accord well with clinical determinations by radiologists. • Several biases, including patients' sex and age, institutions, and radiologists, may cause variable performance by AI bone age models and disagreement between AI and radiologists' clinical determinations of bone age. • AI heatmaps of bone age were not fully consistent with human vision on X-ray films.

Dietary supplementation with microalgae enhances the zebrafish growth performance by modulating immune status and gut microbiota.

Microalgae are known to be abundant in various habitats around the globe, and are rich in high value-added products such as fatty acids, polysaccharides, proteins, and pigments. Microalgae can be exploited as the basic and primitive food source of aquatic animals. We investigated the effects of dietary supplementation with Schizochytrium sp., Spirulina platensis, Chloroella sorokiniana, Chromochloris zofingiensis, and Dunaliella salina on the growth performance, immune status, and intestinal health of zebrafish (Danio rerio). The results showed that these five microalgae diets could improve the feed conversion rate (FCR), especially the D. salina (FCR = 1.02%) and Schizochytrium sp. (FCR = 1.20%) additive groups. Moreover, the microalgae diets decreased the gene expression level of the pro-inflammatory cytokines IL6, IL8, and IL1ß at a normal physiological state of the intestine, especially the Schizochytrium sp., S. platensis, and D. salina dietary groups. The expression of neutrophil marker b7r was increased in the C. sorokiniana diet group; after, the zebrafish were challenged with Vibrio anguillarum, improving the ability to resist this disease. We also found that microalgae diets could regulate the gut microbiota of fish as well as increase the relative abundance of probiotics. To further explain, Cetobacterium was significantly enriched in the S. platensis additive group and Stenotrophomonas was higher in the Schizochytrium sp. additive group than in the other groups. Conversely, harmful bacteria Mycoplasma reduced in all tested microalgae diet groups. Our study indicated that these microalgae could serve as a food source supplement and benefit the health of fish. KEY POINTS: • Microalgae diets enhanced the growth performance of zebrafish. • Microalgae diets attenuated the intestinal inflammatory responses of zebrafish. • Microalgae diets modulated the gut microbiota composition to improve fish health.