Smartphone detection of anemia using patient photos has the potential to provide a non-invasive method of measuring hemoglobin levels, introducing the possibility of increasing the accessibility and cost-effectiveness of current practices. While traditional methods of anemia detection require a complete blood count by a trained healthcare professional, smartphone detection instead relies on the user to take a high quality picture of their fingernails. However, it currently lacks the ability to provide feedback to the user on the quality of their image. For example, an overexposed image or one with low fingernail visibility can lead to inaccurate predictions of hemoglobin levels. We propose that machine learning classification methods can analyze these patient images to estimate the image quality and predict the effectiveness of smartphone detection of anemia for a given image. With various classical machine learning models, we demonstrate and compare the capabilities of each in classifying images of patients’ hands as being of “good” or “bad” quality (or on a more granular numerical scale) when given features of the images. Preliminary results show that a logistic regression model reaches 91.4% accuracy labeling images when compared to empirically assigned labels, and we expect iterative models to achieve improved performance. When completed, we would propose that this classifier could be used in the field to identify if patient image is of high enough quality to produce an accurate measurement of hemoglobin levels in real-time, providing feedback on the phone to adjust or correct the image-taking process.

Ophthalmic drug administration has been increasingly prevalent in recent years, with eyedroppers being utilized to administer costly medication like that for glaucoma. There haven’t been many solutions addressing eyedropper instillation for those with preexisting conditions like arthritis, who often deal with a host of problems when administering them: producing the necessary force to distill a drop, aiming the drop into the eye, and contamination of the eyedropper tip. We are testing the question of whether accessible eye drop aids can significantly improve eyedrop compliance and distillation for the elderly. Solutions to eye drop administration can save money and make the overall process easier for many patients. Existing solutions on the market seem to address the issue of contamination using apparatuses that press onto the lower eyelid, but there is still much to be desired with the force and aim required. Many require the use of gripping or squeezing, motions that many elderly patients can’t apply as much force with. I propose a couple of solutions to these problems in the form of eyedropper aids that each make use of a few different methods, including translating the motion, applying the force with different limbs, and even mechanizing the force required. Through a quantitative study, I hope to eventually test these prototypes through an ophthalmology clinic among a wide variety of elderly. Assessing these prototypes through both questionnaires and observation, I hope to notice an increase in effectiveness from previously existing apparatuses. We will use a survey to ask a variety of questions to around 100 elderly patients with varying expertise in eye drop instillation. The survey will ask whether the tool was more useful, easier, how hard it was to assemble, and we will also monitor quantitatively whether the accuracy of drops actually instilled was better. This work hopefully saves patients money from medication cost from a reduction in wastage, allows for better administration of medicine, and eases the process of distillation.