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Deep Learning and Artificial Intelligence Method for Diagnostics in Laryngology and Masticatory System Based on Medical Imaging and Sensing

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Dear Colleagues,

Nowadays, achievements in medicine and disease diagnosis based on sensors have reached an extraordinary level. Needless to say, progress would hardly be possible without the support of advanced technologies. The enormous possibilities in medical imaging and sensing, along with the development of medical image data handling methods, have formed the foundations for helping patients and making people's health and quality of life significantly better. Having said that, it is important to realize that we, as scientists, play a crucial role in this whole process.

Therefore it is our pleasure to invite scholars to submit their research papers to this Special Issue of the MDPI journal Sensors devoted to the applications of deep learning and computer vision in widely understood laryngology and masticatory system diagnosis. We are looking for your contribution to the field of uni- or multimodal medical image data analysis, the development of improvements in medical imaging quality, supporting the diagnosis of diseases, supported or autonomous medical imagery data analysis, and many other brilliant areas. High-quality research papers addressing, but not limited to, the topic of laryngology or masticatory systems are very welcome. We strongly believe that the Special Issue will be the place where exceptional scientific thoughts meet and will establish new standards for knowledge and scientific experience sharing.

Prospective authors are invited to submit original manuscripts on sensors, topics including but not limited to:

Sensor-based medical image processing in laryngeal/ masticatory system diagnosis;
Computed tomography;
Micro-computed tomography;
Cone beam computer tomography;
Optical coherence tomography;
Magnetic resonance;
Ultrasonography;
Computer-aided diagnosis.

Dr. Sebastian Iwaszenko
Dr. Karolina Nurzynska
Guest Editors

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27 pages, 34070 KiB

Open AccessArticle

Comparison of Faster R-CNN, YOLO, and SSD for Third Molar Angle Detection in Dental Panoramic X-rays

by Piero Vilcapoma, Diana Parra Meléndez, Alejandra Fernández, Ingrid Nicole Vásconez, Nicolás Corona Hillmann, Gustavo Gatica and Juan Pablo Vásconez

Sensors 2024, 24(18), 6053; https://doi.org/10.3390/s24186053 - 19 Sep 2024

Cited by 6 | Viewed by 4383

Abstract

The use of artificial intelligence algorithms (AI) has gained importance for dental applications in recent years. Analyzing AI information from different sensor data such as images or panoramic radiographs (panoramic X-rays) can help to improve medical decisions and achieve early diagnosis of different dental pathologies. In particular, the use of deep learning (DL) techniques based on convolutional neural networks (CNNs) has obtained promising results in dental applications based on images, in which approaches based on classification, detection, and segmentation are being studied with growing interest. However, there are still several challenges to be tackled, such as the data quality and quantity, the variability among categories, and the analysis of the possible bias and variance associated with each dataset distribution. This study aims to compare the performance of three deep learning object detection models—Faster R-CNN, YOLO V2, and SSD—using different ResNet architectures (ResNet-18, ResNet-50, and ResNet-101) as feature extractors for detecting and classifying third molar angles in panoramic X-rays according to Winter’s classification criterion. Each object detection architecture was trained, calibrated, validated, and tested with three different feature extraction CNNs which are ResNet-18, ResNet-50, and ResNet-101, which were the networks that best fit our dataset distribution. Based on such detection networks, we detect four different categories of angles in third molars using panoramic X-rays by using Winter’s classification criterion. This criterion characterizes the third molar’s position relative to the second molar’s longitudinal axis. The detected categories for the third molars are distoangular, vertical, mesioangular, and horizontal. For training, we used a total of 644 panoramic X-rays. The results obtained in the testing dataset reached up to 99% mean average accuracy performance, demonstrating the YOLOV2 obtained higher effectiveness in solving the third molar angle detection problem. These results demonstrate that the use of CNNs for object detection in panoramic radiographs represents a promising solution in dental applications. Full article

(This article belongs to the Special Issue Deep Learning and Artificial Intelligence Method for Diagnostics in Laryngology and Masticatory System Based on Medical Imaging and Sensing)

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