IMPORTANCE Opioids, which carry a high risk for addiction and overdose, are commonly prescribed after corneal surgery. Data are lacking describing opioid prescribing practices and opioid needs by patients after ophthalmic surgery.OBJECTIVES To quantify opioid use and to assess the association of decreasing the number of opioid tablets prescribed after corneal surgery with postsurgical use.
DESIGN, SETTING, AND PARTICIPANTSThis prospective cohort study investigated opioid use after corneal surgery using direct interviews of 2 adult patient cohorts separated by an updated opioid prescribing guideline. The first cohort survey assessed the quantity of opioid tablets used after surgery. The cornea division of a tertiary care academic medical center reviewed the use needs and decreased the number of tablets prescribed after routine cases. Simultaneously, a statewide opioid monitoring program began that provided patients with opioid information. A second unique cohort received a more detailed survey to assess use, opioid disposal, and pain control. Data for the first cohort were collected from
Purpose
To develop a method for accurate automated real-time identification of instruments in cataract surgery videos.
Methods
Cataract surgery videos were collected at University of Michigan's Kellogg Eye Center between 2020 and 2021. Videos were annotated for the presence of instruments to aid in the development, validation, and testing of machine learning (ML) models for multiclass, multilabel instrument identification.
Results
A new cataract surgery database, BigCat, was assembled, containing 190 videos with over 3.9 million annotated frames, the largest reported cataract surgery annotation database to date. Using a dense convolutional neural network (CNN) and a recursive averaging method, we were able to achieve a test F1 score of 0.9528 and test area under the receiver operator characteristic curve of 0.9985 for surgical instrument identification. These prove to be state-of-the-art results compared to previous works, while also only using a fraction of the model parameters of the previous architectures.
Conclusions
Accurate automated surgical instrument identification is possible with lightweight CNNs and large datasets. Increasingly complex model architecture is not necessary to retain a well-performing model. Recurrent neural network architectures add additional complexity to a model and are unnecessary to attain state-of-the-art performance.
Translational Relevance
Instrument identification in the operative field can be used for further applications such as evaluating surgical trainee skill level and developing early warning detection systems for use during surgery.
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