Research
The TEAM Project aims to explore the use of artificial intelligence (AI) in dentistry and maxillofacial surgery, focusing on developing tools for pathology screening, clinical decision support, and surgical planning.
By integrating AI into these critical areas, the project seeks to enhance diagnostic accuracy, improve treatment outcomes, and streamline surgical procedures.
Our research has the potential to support clinicians in making more informed decisions and to streamline surgical procedures, addressing some of the existing challenges in the field.
Publications
An Examination of Temporomandibular Joint Disc Displacement through Magnetic Resonance Imaging by Integrating Artificial Intelligence: Preliminary Findings
Background and Objectives: This research was aimed at constructing a complete automated temporomandibular joint disc position identification system that could assist with magnetic resonance imaging disc displacement diagnosis on oblique sagittal and oblique coronal images.
Materials and Methods: The study included fifty subjects with magnetic resonance imaging scans of the temporomandibular joint. Oblique sagittal and coronal sections of the magnetic resonance imaging scans were analyzed. Investigations were performed on the right and left coronal images with a closed mouth, as well as right and left sagittal images with closed and open mouths. Three hundred sagittal and coronal images were employed to train the artificial intelligence algorithm.
Results: The accuracy ratio of the completely computerized articular disc identification method was 81%.
Conclusions: An automated and accurate evaluation of temporomandibular joint disc position was developed by using both oblique sagittal and oblique coronal magnetic resonance imaging images.
Published in
Almășan O, Mureșanu S, Hedeșiu P, Cotor A, Băciuț M, Roman R, TEAM Project Group. An Examination of Temporomandibular Joint Disc Displacement through Magnetic Resonance Imaging by Integrating Artificial Intelligence: Preliminary Findings. Medicina. 2024; 60(9):1396. https://doi.org/10.3390/medicina60091396
Teeth segmentation and carious lesions segmentation in panoramic X-ray images using CariSeg, a networks’ ensemble
Background: Dental cavities are common oral diseases that can lead to pain, discomfort, and eventually, tooth loss. Early detection and treatment of cavities can prevent these negative consequences. We propose CariSeg, an intelligent system composed of four neural networks that result in the detection of cavities in dental X-rays with 99.42% accuracy.
Method: The first model of CariSeg, trained using the U-Net architecture, segments the area of interest, the teeth, and crops the radiograph around it. The next component segments the carious lesions and it is an ensemble composed of three architectures: U-Net, Feature Pyramid Network, and DeeplabV3. For tooth identification two merged datasets were used: The Tufts Dental Database consisting of 1000 panoramic radiography images and another dataset of 116 anonymized panoramic X-rays, taken at Noor Medical Imaging Center, Qom. For carious lesion segmentation, a dataset consisting of 150 panoramic X-ray images was acquired from the Department of Oral and Maxillofacial Surgery and Radiology, Iuliu Hatieganu University of Medicine and Pharmacy, Cluj-Napoca.
Results: The experiments demonstrate that our approach results in 99.42% accuracy and a mean 68.2% Dice coefficient. Conclusions: AI helps in detecting carious lesions by analyzing dental X-rays and identifying cavities that might be missed by human observers, leading to earlier detection and treatment of cavities and resulting in better oral health outcomes.
Published in
Mărginean AC, Mureşanu S, Hedeşiu M, Dioşan L. Teeth segmentation and carious lesions segmentation in panoramic X-ray images using CariSeg, a networks’ ensemble. Heliyon. 2024 May 10;10(10):e30836. doi: 10.1016/j.heliyon.2024.e30836. PMID: 38803980; PMCID: PMC11128823.
Short-term aesthetic rehabilitation with 3d printed snap-on smile devices – literature review and a case report
Aim of the study: The main objective of this paper is to perform an up-to-date literature review of the application and implications of using Snap-On Smile devices for short-term aesthetic rehabilitation, as well as to showcase the 3D printing workflow in manufacturing these devices through a case study.
Materials and methods: The present systematic study was conducted following the PRISMA-P structure of a systematic review, by using three electronic databases (PubMed, Google Scholar, and EMBASE) to perform a literature search from 2000 to 2024 using the following MESH terms: snap on smile, aesthetic, removable, device. After discarding duplicates, out of 81 results, a total of 6 articles were eligible and included in the review.
Results: Constructed from durable materials like crystallized acetyl resin or PMMA, the Snap-On Smile offers up to five years of use. Manufactured through advanced techniques such as injection moulding, CAD/CAM milling, or 3D printing, it provides an aesthetic solution without necessitating tooth preparation. The case study of a 59-year-old female illustrates its efficacy in providing immediate aesthetic enhancement for a significant social event.
Conclusions: The Snap-On Smile has emerged as a significant advancement in aesthetic dentistry, balancing the desire for improved dental aesthetics with the need to preserve natural tooth structures. Its ease of application and non-invasive nature make it a suitable option for many patients, though its limitations necessitate careful consideration in complex cases.
Published in
Burde AV, Frățilă C, Varvară EB, Varvară AV, Short-term aesthetic rehabilitation with 3d printed snap-on smile devices – literature review and a case report, Romanian Journal of Oral Rehabilitation, Vol. 16, No.2 April-June 2024, DOI: 10.6261/RJOR.2024.2.16.58
Automating Dental Condition Detection on Panoramic Radiographs: Challenges, Pitfalls, and Opportunities
Background/Objectives: The integration of AI into dentistry holds promise for improving diagnostic workflows, particularly in the detection of dental pathologies and pre-radiotherapy screening for head and neck cancer patients. This study aimed to develop and validate an AI model for detecting various dental conditions, with a focus on identifying teeth at risk prior to radiotherapy.
Methods: A YOLOv8 model was trained on a dataset of 1628 annotated panoramic radiographs and externally validated on 180 radiographs from multiple centers. The model was designed to detect a variety of dental conditions, including periapical lesions, impacted teeth, root fragments, prosthetic restorations, and orthodontic devices.
Results: The model showed strong performance in detecting implants, endodontic treatments, and surgical devices, with precision and recall values exceeding 0.8 for several conditions. However, performance declined during external validation, highlighting the need for improvements in generalizability.
Conclusions: YOLOv8 demonstrated robust detection capabilities for several dental conditions, especially in training data. However, further refinement is needed to enhance generalizability in external datasets and improve performance for conditions like periapical lesions and bone loss.
Published in
Mureșanu S, Hedeșiu M, Iacob L, Eftimie R, Olariu E, Dinu C, Jacobs R, On Behalf Of Team Project Group. Automating Dental Condition Detection on Panoramic Radiographs: Challenges, Pitfalls, and Opportunities. Diagnostics (Basel). 2024 Oct 21;14(20):2336. doi: 10.3390/diagnostics14202336. PMID: 39451659; PMCID: PMC11507083.