ReviewInternational journal of computer assisted radiology and surgery2023
Image-based recognition of surgical instruments by means of convolutional neural networks.
Review in International journal of computer assisted radiology and surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis work presents a novel camera-based approach for the visual recognition of surgical instruments. In contrast to the state of the art, the presented approach works without any additional markers. The recognition is the first step for the implementation of tracking and tracing of instruments wherever they are visible and could be seen by camera systems. Recognition takes place at item number level. Surgical instruments that share the same article number also share the same functions. A distinction at this level of detail is sufficient for most clinical applications.
methodsIn this work, an image-based data set with over 6500 images is generated from 156 different surgical instruments. Forty-two images were acquired from each surgical instrument. The largest part is used to train convolutional neural networks (CNNs). The CNN is used as a classifier, where each class corresponds to an article number of the surgical instruments used. Only one surgical instrument exists per article number in the data set.
resultsWith a suitable amount of validation and test data, different CNN approaches are evaluated. The results show a recognition accuracy of up to 99.9% for the test data. To achieve these accuracies, an EfficientNet-B7 was used. It was also pre-trained on the ImageNet data set and then fine-tuned on the given data. This means that no weights were frozen during the training, but all layers were trained.
conclusionWith recognition accuracies of up to 99.9% on a highly meaningful test data set, recognition of surgical instruments is suitable for many track and trace applications in the hospital. But the system has limitations: A homogeneous background and controlled lighting conditions are required. The detection of multiple instruments in one image in front of various backgrounds is part of future work.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.