To evaluate the capabilities and readiness of deep learning technology to improve dermal exposure estimates, the authors conducted a proof-of-concept study focused on quantification of exposed skin areas from images of indoor painting. Quantifying the surface area of skin that comes into contact with the chemical being assessed is an important aspect of dermal exposure assessments. Often, this parameter is qualitatively estimated based on assumptions such as typical clothing worn or body parts exposed and estimated average surface area of the body parts. For example, the AIHA IH SkinPerm tool has a default exposed skin surface area of 1,000 cm², which is the estimated surface area of two adult hands. While our study focused on consumer product use, the approach we developed can be applied to workplace tasks where dermal exposure may be a concern. We also evaluated the performance of the computer-based approach by comparing the deep learning results with human estimations. TECHNOLOGY DEVELOPMENT AND RESULTS Computer vision is a field of artificial intelligence that enables computers to detect and interpret images and videos. It has rapidly advanced from traditional color-based approaches to many recent developments that leverage deep learning. When, as is often the case, no off-the-shelf computer vision method exists for specific applications, the solution typically requires transferring learning from existing architectures and then customizing them with relevant images. To train and evaluate our model for this study, we purchased one hundred seventy pictures of indoor painting scenarios from Getty Images, processed them, and analyzed them using different computer vision approaches. To benchmark our model’s performance, humans visually identified the exposed body parts in the images and converted them into percentages based on the standard body-part-to-whole-body ratio for adult populations. We used the VGG Image Annotator tool, or VIA, to label pictures in Common Objects in Context (COCO), a standard format for storing and sharing annotations of images and videos. We started with a traditional computer vision method. Unlike deep learning, this method does not require a large number of training images and can detect the color segmentation of objects in pictures based on their color space values. Color spaces are a fundamental concept in image processing and play a crucial role in computer vision. IBM defines a color space as “a representation of the individual colors that can be combined to create other colors.” Because devices such as computer and TV monitors, printers, and scanners create colors differently, different methods of describing colors are needed for each device. The color space defines a standardized method of representing colors in digital images, allowing for efficient processing and analysis. RGB (Red Green Blue) is the most commonly used color space in digital imaging, but other color spaces, such as CMYK, HSV, and YUV, are also widely used.