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- Title
Image Analysis System for Early Detection of Cardiothoracic Surgery Wound Alterations Based on Artificial Intelligence Models.
- Authors
Pereira, Catarina; Guede-Fernández, Federico; Vigário, Ricardo; Coelho, Pedro; Fragata, José; Londral, Ana
- Abstract
Cardiothoracic surgery patients have the risk of developing surgical site infections which cause hospital readmissions, increase healthcare costs, and may lead to mortality. This work aims to tackle the problem of surgical site infections by predicting the existence of worrying alterations in wound images with a wound image analysis system based on artificial intelligence. The developed system comprises a deep learning segmentation model (MobileNet-Unet), which detects the wound region area and categorizes the wound type (chest, drain, and leg), and a machine learning classification model, which predicts the occurrence of wound alterations (random forest, support vector machine and k-nearest neighbors for chest, drain, and leg, respectively). The deep learning model segments the image and assigns the wound type. Then, the machine learning models classify the images from a group of color and textural features extracted from the output region of interest to feed one of the three wound-type classifiers that reach the final binary decision of wound alteration. The segmentation model achieved a mean Intersection over Union of 89.9% and a mean average precision of 90.1%. Separating the final classification into different classifiers was more effective than a single classifier for all the wound types. The leg wound classifier exhibited the best results with an 87.6% recall and 52.6% precision.
- Subjects
DEEP learning; SURGICAL site; ARTIFICIAL intelligence; IMAGE analysis; IMAGING systems; SURGICAL site infections
- Publication
Applied Sciences (2076-3417), 2023, Vol 13, Issue 4, p2120
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app13042120