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Author: Admin | 2025-04-28
Collected about 10k pills in all kinds of environment from all over the world, of which I’ve only been able to ID about 8k US drugs, many of which top sites do not have an image for (try searching Round Pink E 345). I firmly believe that PillSync will soon have the most comprehensive database of Pill Photos on the internet.Surprisingly, there are about 1,500 pills that I cannot locate even after a thorough Google Search. These pills include international generics, streets, and fake drugs that exploded during the opioid crisis. Here’s a foreign generic viagra pill. I’m able to ID about 200 drugs from Canada, Australia, and the UK. A lot more is probably still in the batch of unidentified photos so feel free to help!Today, I can confidently tell you that even with the pill photo, it can be hard and time-consuming to track down a drug as many pills are just so similar that the basic parameters of imprint, shape, color, and score are not enough. Try this search of Round White Logo. Google is severely inadequate here. As someone who has ID thousands of pills everyday, I wish there was a better way. After all, I cannot scale this up as the need grows to thousands of pills a day.Pill Recognition — the age of Machine LearningAs it turns out, another tech trend comes to the rescue. The past few years have seen a democratization in Machine Learning (ML) that allows computers to see images. More specifically, Object Detection can pinpoint the object and Classification can tell you what that object is. The challenge is training it with a large amount of real-world data. There is no replacement for data found in the wild and chaotic environment. You cannot simulate this. This is why Tesla leads the pack with real-world self-driving dataset flowing in everyday from Teslas on the road.Likewise, PillSync is the largest and only pill dataset of its kind leading the APR pill AI revolution. In 2016, a different team from NLM launched an APR challenge using about 40k pill photos taken under different background/lighting conditions. I’ve been analyzing these pills and concluded that they are not the same as pills users submitted over the years. At this time, I’m not sure if I can use the NLM set in training as they may skew the algorithm’s ability to ID pills found in real-world conditions.Of the 8k US drugs identified over the last 10 years, about 3,000 are unique pills. This means that:~10k prescription/OTC drugs, only 3k drugs are found in ~90% of user-submitted pillsIn order to accurately auto ID these 3,000 pills, I would need ~100 sample photos of each pill to train.
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