  Every year, millions of pounds of medicines remain unused in the U.S. and are
subject to an in-home disposal, i.e., kept in medicine cabinets, flushed in
toilet or thrown in regular trash. In-home disposal, however, can negatively
impact the environment and public health. The drug take-back programs (drug
take-backs) sponsored by the Drug Enforcement Administration (DEA) and its
state and industry partners collect unused consumer medications and provide the
best alternative to in-home disposal of medicines. However, the drug take-backs
are expensive to operate and not widely available. In this paper, we show that
artificial intelligence (AI) can be applied to drug take-backs to render them
operationally more efficient. Since identification of any waste is crucial to a
proper disposal, we showed that it is possible to accurately identify loose
consumer medications solely based on the physical features and visual
appearance. We have developed an automatic technique that uses deep neural
networks and computer vision to identify and segregate solid medicines. We
applied the technique to images of about one thousand loose pills and succeeded
in correctly identifying the pills with an accuracy of 0.912 and top-5 accuracy
of 0.984. We also showed that hazardous pills could be distinguished from
non-hazardous pills within the dataset with an accuracy of 0.984. We believe
that the power of artificial intelligence could be harnessed in products that
would facilitate the operation of the drug take-backs more efficiently and help
them become widely available throughout the country.
