An ingenious deep learning approach for pressure injury depth evaluation with limited data.

BACKGROUND: The development of models using deep learning (DL) to assess pressure injuries from wound images has recently gained attention. Creating enough supervised data is important for improving performance but is time-consuming. Therefore, the development of models that can achieve high performance with limited supervised data is desirable. MATERIALS AND METHODS: This retrospective observational study utilized DL and included patients who received medical examinations for sacral pressure injuries between February 2017 and December 2021. Images were labeled according to the DESIGN-R® classification. Three artificial intelligence (AI) models for assessing pressure injury depth were created with a convolutional neural network (Categorical, Binary, and Combined classification models) and performance was compared among the models. RESULTS: A set of 414 pressure injury images in five depth stages (d0 to D4) were analyzed. The Combined classification model showed superior performance (F1-score, 0.868). The Categorical classification model frequently misclassified d1 and d2 as d0 (d0 Precision, 0.503), but showed high performance for D3 and D4 (F1-score, 0.986 and 0.966, respectively). The Binary classification model showed high performance in differentiating between d0 and d1-D4 (F1-score, 0.895); however, performance decreased with increasing number of evaluation steps. CONCLUSION: The Combined classification model displayed superior performance without increasing the supervised data, which can be attributed to use of the high-performance Binary classification model for initial d0 evaluation and subsequent use of the Categorical classification model with fewer evaluation steps. Understanding the unique characteristics of classification methods and deploying them appropriately can enhance AI model performance.

Glottis Recognition Software Development Using Artificial Intelligence.

The use of video laryngoscopes has enhanced the visualization of the vocal cords, thereby improving the accessibility of tracheal intubation. Employing artificial intelligence (AI) to recognize images obtained through video laryngoscopy, particularly when marking the epiglottis and vocal cords, may elucidate anatomical structures and enhance anatomical comprehension of anatomy. This study investigates the ability of an AI model to accurately identify the glottis in video laryngoscope images captured from a manikin. Tracheal intubation was conducted on a manikin using a bronchoscope with recording capabilities, and image data of the glottis was gathered for creating an AI model. Data preprocessing and annotation of the vocal cords, epiglottis, and glottis were performed, and human annotation of the vocal cords, epiglottis, and glottis was carried out. Based on the AI's determinations, anatomical structures were color-coded for identification. The recognition accuracy of the epiglottis and vocal cords recognized by the AI model was 0.9516, which was over 95%. The AI successfully marked the glottis, epiglottis, and vocal cords during the tracheal intubation process. These markings significantly aided in the visual identification of the respective structures with an accuracy of more than 95%. The AI demonstrated the ability to recognize the epiglottis, vocal cords, and glottis using an image recognition model of a manikin.