Computer vision model for the detection of canine pododermatitis and neoplasia of the paw.

BACKGROUND: Artificial intelligence (AI) has been used successfully in human dermatology. AI utilises convolutional neural networks (CNN) to accomplish tasks such as image classification, object detection and segmentation, facilitating early diagnosis. Computer vision (CV), a field of AI, has shown great results in detecting signs of human skin diseases. Canine paw skin diseases are a common problem in general veterinary practice, and computer vision tools could facilitate the detection and monitoring of disease processes. Currently, no such tool is available in veterinary dermatology. ANIMALS: Digital images of paws from healthy dogs and paws with pododermatitis or neoplasia were used. OBJECTIVES: We tested the novel object detection model Pawgnosis, a Tiny YOLOv4 image analysis model deployed on a microcomputer with a camera for the rapid detection of canine pododermatitis and neoplasia. MATERIALS AND METHODS: The prediction performance metrics used to evaluate the models included mean average precision (mAP), precision, recall, average precision (AP) for accuracy and frames per second (FPS) for speed. RESULTS: A large dataset labelled by a single individual (Dataset A) used to train a Tiny YOLOv4 model provided the best results with a mean mAP of 0.95, precision of 0.86, recall of 0.93 and 20 FPS. CONCLUSIONS AND CLINICAL RELEVANCE: This novel object detection model has the potential for application in the field of veterinary dermatology.

Labor Force Participation and Hearing Loss Among Adults in the United States: Evidence From the National Health and Nutrition Examination Survey.

PURPOSE: The purpose of this article was to study the association between hearing loss (HL) and labor force participation in the National Health and Nutrition Examination Survey (NHANES). METHOD: This cross-sectional study used data from the 1999-2000, 2001-2002, 2003-2004, 2011-2012, and 2015-2016 cycles of the NHANES. The sample was restricted to adults aged 25-65 years with complete audiometric data. HL was defined based on the pure-tone average (PTA) of 0.5-, 1-, 2-, and 4-kHz thresholds in the better hearing ear as follows: no loss (PTA < 25 dB), mild HL (25 dB < PTA < 40 dB), and moderate-to-severe HL (PTA > 40 dB). The association between HL and labor force participation was estimated using weighted logistic regression adjusted for age, sex, race/ethnicity, education, living arrangements, and health status. RESULTS: In a sample of 9,963 participants (50.6% women, 22.6% Black, 27% Hispanic), we found that compared with adults without HL, individuals with moderate-to-severe HL had greater odds of being outside of the labor force (odds ratio = 2.35; 95% confidence interval: 1.42-3.88). However, there were no differences by HL status in being employed or having a full- versus part-time job. CONCLUSIONS: Moderate-to-severe HL, but not mild HL, was associated with higher odds of not participating in the labor force. However, there were no differences by HL status in being employed or having a full- versus part-time job. Further research is needed to better characterize how HL may affect labor force participation. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.19858930.