Deep Learning Models for the Screening of Cognitive Impairment Using Multimodal Fundus Images.

OBJECTIVE: We aimed to develop a deep learning system capable of identifying subjects with cognitive impairment quickly and easily based on multimodal ocular images. DESIGN: Cross sectional study. SUBJECTS: Participants of Beijing Eye Study 2011 and patients attending Beijing Tongren Eye Center and Beijing Tongren Hospital Physical Examination Center. METHODS: We trained and validated a deep learning algorithm to assess cognitive impairment using retrospectively collected data from the Beijing Eye Study 2011. Cognitive impairment was defined as a Mini-Mental State Examination score < 24. Based on fundus photographs and OCT images, we developed 5 models based on the following sets of images: macula-centered fundus photographs, optic disc-centered fundus photographs, fundus photographs of both fields, OCT images, and fundus photographs of both fields with OCT (multimodal). The performance of the models was evaluated and compared in an external validation data set, which was collected from patients attending Beijing Tongren Eye Center and Beijing Tongren Hospital Physical Examination Center. MAIN OUTCOME MEASURES: Area under the curve (AUC). RESULTS: A total of 9424 retinal photographs and 4712 OCT images were used to develop the model. The external validation sets from each center included 1180 fundus photographs and 590 OCT images. Model comparison revealed that the multimodal performed best, achieving an AUC of 0.820 in the internal validation set, 0.786 in external validation set 1, and 0.784 in external validation set 2. We evaluated the performance of the multi-model in different sexes and different age groups; there were no significant differences. The heatmap analysis showed that signals around the optic disc in fundus photographs and the retina and choroid around the macular and optic disc regions in OCT images were used by the multimodal to identify participants with cognitive impairment. CONCLUSIONS: Fundus photographs and OCT can provide valuable information on cognitive function. Multimodal models provide richer information compared with single-mode models. Deep learning algorithms based on multimodal retinal images may be capable of screening cognitive impairment. This technique has potential value for broader implementation in community-based screening or clinic settings. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

[Soft and hard tissue changes and vertical correlation of early skeletal Class â ¢ malocclusion treated with Sander â ¢ appliance].

PURPOSE: To explore the correlation between the soft and hard tissue changes and the vertical direction of early skeletal Class Ⅲ malocclusion treated with Sander Ⅲ appliance. METHODS: Thirty-two patients with skeletal Class Ⅲ malocclusion who underwent Sander Ⅲ appliance correction were enrolled. The changes of soft and hard tissues were observed before and after treatment for 12 months. The correlation between soft tissue and vertical direction was analyzed. SPSS 25.0 software package was used for statistical analysis. RESULTS: After 12 months of treatment, SNB decreased (P<0.05), ANB, A-PTV, Go-Me increased (P<0.05), soft tissue index LL-LI increased (P<0.05), LL-EP decreased (P<0.05). L1/MP was reduced after treatment (P<0.05), and U1E-PTV was increased (P<0.05). Correlation analysis showed that UL-UI was positively correlated with tilt angle, U1E-PP and U6E-PP(P<0.05); LL-LI was negatively correlated with tilt angle, U1E-PP and U6E-PP (P<0.05). Sn -UL/FH and U6E-PP were positively correlated (P<0.05). CONCLUSIONS: Sander Ⅲ appliance can effectively correct the soft and hard tissue deformity of patients with early skeletal Class Ⅲ malocclusion. The shape of the lip tends to be coordinated with the improvement of hard tissue. The soft tissue index is closely related to the vertical direction of the hard tissue.