Objective: To evaluate the diagnostic capability of artificial intelligence (AI) for detecting and classifying odontogenic cysts and tumors, with special emphasis on odontogenic keratocyst (OKC) and ameloblastoma.
Study Design: Nine electronic databases and the gray literature were examined. Human-based studies using AI algorithms to detect or classify odontogenic cysts and tumors by using panoramic radiographs or CBCT were included. Diagnostic tests were evaluated, and a meta-analysis was performed for classifying OKCs and ameloblastomas. Heterogeneity, risk of bias, and certainty of evidence were evaluated.
Results: Twelve studies concluded that AI is a promising tool for the detection and/or classification of lesions, producing high diagnostic test values. Three articles assessed the sensitivity of convolutional neural networks in classifying similar lesions using panoramic radiographs, specifically OKC and ameloblastoma. The accuracy was 0.893 (95% CI 0.832-0.954). AI applied to cone beam computed tomography produced superior accuracy based on only 4 studies. The results revealed heterogeneity in the models used, variations in imaging examinations, and discrepancies in the presentation of metrics.
Conclusion: AI tools exhibited a relatively high level of accuracy in detecting and classifying OKC and ameloblastoma. Panoramic radiography appears to be an accurate method for AI-based classification of these lesions, albeit with a low level of certainty. The accuracy of CBCT model data appears to be high and promising, although with limited available data.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.oooo.2024.03.004 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
InPAS: An R/Bioconductor Package for Identifying Novel Polyadenylation Sites and Alternative Polyadenylation from Bulk RNA-seq Data.
Front Biosci (Schol Ed)
December 2024
Department of Molecular, Cell and Cancer Biology, University of Massachusetts Chan Medical School, Worcester, MA 01605, USA.
Background: Alternative cleavage and polyadenylation (APA) is a crucial post-transcriptional gene regulation mechanism that regulates gene expression in eukaryotes by increasing the diversity and complexity of both the transcriptome and proteome. Despite the development of more than a dozen experimental methods over the last decade to identify and quantify APA events, widespread adoption of these methods has been limited by technical, financial, and time constraints. Consequently, APA remains poorly understood in most eukaryotes.
View Article and Find Full Text PDFClear Cell Papillary Renal Cell Carcinoma: Case Series with Review of the Literature.
J West Afr Coll Surg
July 2024
Department of Pathology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India.
Clear cell papillary renal cell carcinoma (CCPRCC) was included as a separate entity in the World Health Organisation classification of renal tumours in 2016. Immuno-histopathological and genetic characteristics are much known, but still, clinical features and long-term follow-up require more consolidated data. We report three cases of CCPRCC, detected in different clinical settings.
View Article and Find Full Text PDFWastewater-based epidemiology: deriving a SARS-CoV-2 data validation method to assess data quality and to improve trend recognition.
Front Public Health
December 2024
Wastewater Technology Research, Wastewater Disposal, German Environment Agency, Berlin, Germany.
Introduction: Accurate and consistent data play a critical role in enabling health officials to make informed decisions regarding emerging trends in SARS-CoV-2 infections. Alongside traditional indicators such as the 7-day-incidence rate, wastewater-based epidemiology can provide valuable insights into SARS-CoV-2 concentration changes. However, the wastewater compositions and wastewater systems are rather complex.
View Article and Find Full Text PDFDeep learning-based automated diagnosis of temporomandibular joint anterior disc displacement and its clinical application.
Front Physiol
December 2024
Department of Oral & Maxillofacial Surgery, Shenzhen Stomatology Hospital, Affiliated to Shenzhen University, Shenzhen, Guangdong Province, China.
Introduction: This study aimed to develop a deep learning-based method for interpreting magnetic resonance imaging (MRI) scans of temporomandibular joint (TMJ) anterior disc displacement (ADD) and to formulate an automated diagnostic system for clinical practice.
Methods: The deep learning models were utilized to identify regions of interest (ROI), segment TMJ structures including the articular disc, condyle, glenoid fossa, and articular tubercle, and classify TMJ ADD. The models employed Grad-CAM heatmaps and segmentation annotation diagrams for visual diagnostic predictions and were deployed for clinical application.
Dye amount quantification of Papanicolaou-stained cytological images by multispectral unmixing: spectral analysis of cytoplasmic mucin.
J Med Imaging (Bellingham)
January 2025
Okaya City Hospital, Division of Diagnostic Pathology, Okaya, Japan.
Purpose: The color of Papanicolaou-stained specimens is a crucial feature in cytology diagnosis. However, the quantification of color using digital images is challenging due to the variations in the staining process and characteristics of imaging equipment. The dye amount estimation of stained specimens is helpful for quantitatively interpreting the color based on a physical model.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!