1
|
Milani OH, Atici SF, Allareddy V, Ramachandran V, Ansari R, Cetin AE, Elnagar MH. A fully automated classification of third molar development stages using deep learning. Sci Rep 2024; 14:13082. [PMID: 38844566 PMCID: PMC11156840 DOI: 10.1038/s41598-024-63744-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 05/31/2024] [Indexed: 06/09/2024] Open
Abstract
Accurate classification of tooth development stages from orthopantomograms (OPG) is crucial for dental diagnosis, treatment planning, age assessment, and forensic applications. This study aims to develop an automated method for classifying third molar development stages using OPGs. Initially, our data consisted of 3422 OPG images, each classified and curated by expert evaluators. The dataset includes images from both Q3 (lower jaw left side) and Q4 (lower right side) regions extracted from panoramic images, resulting in a total of 6624 images for analysis. Following data collection, the methodology employs region of interest extraction, pre-filtering, and extensive data augmentation techniques to enhance classification accuracy. The deep neural network model, including architectures such as EfficientNet, EfficientNetV2, MobileNet Large, MobileNet Small, ResNet18, and ShuffleNet, is optimized for this task. Our findings indicate that EfficientNet achieved the highest classification accuracy at 83.7%. Other architectures achieved accuracies ranging from 71.57 to 82.03%. The variation in performance across architectures highlights the influence of model complexity and task-specific features on classification accuracy. This research introduces a novel machine learning model designed to accurately estimate the development stages of lower wisdom teeth in OPG images, contributing to the fields of dental diagnostics and treatment planning.
Collapse
Affiliation(s)
- Omid Halimi Milani
- Department of Electrical and Computer Engineering, University of Illinois Chicago, Chicago, IL, USA
| | - Salih Furkan Atici
- Department of Electrical and Computer Engineering, University of Illinois Chicago, Chicago, IL, USA
| | - Veerasathpurush Allareddy
- Department of Orthodontics (M/C 841), College of Dentistry, University of Illinois Chicago, 801 S. Paulina Street, RM 131, Chicago, IL, 60612-7211, USA
| | - Vinitha Ramachandran
- Department of Orthodontics (M/C 841), College of Dentistry, University of Illinois Chicago, 801 S. Paulina Street, RM 131, Chicago, IL, 60612-7211, USA
| | - Rashid Ansari
- Department of Electrical and Computer Engineering, University of Illinois Chicago, Chicago, IL, USA
| | - Ahmet Enis Cetin
- Department of Electrical and Computer Engineering, University of Illinois Chicago, Chicago, IL, USA
| | - Mohammed H Elnagar
- Department of Orthodontics (M/C 841), College of Dentistry, University of Illinois Chicago, 801 S. Paulina Street, RM 131, Chicago, IL, 60612-7211, USA.
| |
Collapse
|
2
|
Ardabili SZ, Bahmani S, Lahijan LZ, Khaleghi N, Sheykhivand S, Danishvar S. A Novel Approach for Automatic Detection of Driver Fatigue Using EEG Signals Based on Graph Convolutional Networks. SENSORS (BASEL, SWITZERLAND) 2024; 24:364. [PMID: 38257457 PMCID: PMC10819416 DOI: 10.3390/s24020364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 12/27/2023] [Accepted: 01/04/2024] [Indexed: 01/24/2024]
Abstract
Nowadays, the automatic detection of driver fatigue has become one of the important measures to prevent traffic accidents. For this purpose, a lot of research has been conducted in this field in recent years. However, the diagnosis of fatigue in recent research is binary and has no operational capability. This research presents a multi-class driver fatigue detection system based on electroencephalography (EEG) signals using deep learning networks. In the proposed system, a standard driving simulator has been designed, and a database has been collected based on the recording of EEG signals from 20 participants in five different classes of fatigue. In addition to self-report questionnaires, changes in physiological patterns are used to confirm the various stages of weariness in the suggested model. To pre-process and process the signal, a combination of generative adversarial networks (GAN) and graph convolutional networks (GCN) has been used. The proposed deep model includes five convolutional graph layers, one dense layer, and one fully connected layer. The accuracy obtained for the proposed model is 99%, 97%, 96%, and 91%, respectively, for the four different considered practical cases. The proposed model is compared to one developed through recent methods and research and has a promising performance.
Collapse
Affiliation(s)
- Sevda Zafarmandi Ardabili
- Electrical and Computer Engineering Department, Southern Methodist University, Dallas, TX 75205, USA
| | - Soufia Bahmani
- Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran 15875-4413, Iran
| | - Lida Zare Lahijan
- Biomedical Engineering Department, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 51666-16471, Iran
| | - Nastaran Khaleghi
- Biomedical Engineering Department, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 51666-16471, Iran
| | - Sobhan Sheykhivand
- Department of Biomedical Engineering, University of Bonab, Bonab 55517-61167, Iran;
| | - Sebelan Danishvar
- College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UK
| |
Collapse
|