Effect of the reduction of artefacts arising from high-density materials on cone-beam ct by artificial intelligence on the diagnosis of vertical root fracture and root perforation

• Grant number: 22/07081-5
• Support Opportunities: Scholarships abroad - Research Internship - Post-doctor
• Start date: November 01, 2022
• End date: October 31, 2023
• Field of knowledge: Health Sciences - Dentistry - Endodontics
• Principal Investigator: Manoel Damiao de Sousa Neto
• Grantee: Amanda Candemil Kanashiro
• Supervisor: Filippo Gatti
• Host Institution: Faculdade de Odontologia de Ribeirão Preto (FORP). Universidade de São Paulo (USP). Ribeirão Preto , SP, Brazil
• Institution abroad: École Centrale Paris, France
• Associated to the scholarship: 21/01623-8 - Effect of the reduction of artefacts arising from high-density materials on cone-beam CT by artificial intelligence on the diagnosis of vertical root fracture and root perforation, BP.PD

Abstract

Cone-beam computed tomography (CBCT) is an imaging exam used in endodontics to diagnose diseases and evaluate internal root anatomy, however, one of its limitations is the presence ofartefacts that hamper the diagnostic efficacy. Although the literature presents different techniquesto reduce artefacts, till this moment, there are no effective methods that contribute to its reduction. Deep convolutional neural network (DCNN) has shown promising approaches to reduce thepresence of artefacts arising from high-density materials, however, besides the scientific literatureonly presents preliminary data, with neural networks with low training by few CBCT images,there is no information about its impact on diagnostic accuracy of clinical tasks such as rootfracture and perforation. Therefore, this study aims to create a DCNN to reduce artefacts arisingfrom high-density materials and low X-ray dose, based in CBCT scans, evaluate the impact in thediagnostic accuracy of different clinical tasks and analyze and compare its efficacy with othersartefact reduction methods. The Ad-hoc DCNN initially design will be corrected in thecomputational facilities of Mésocentre Moulon (Ruche - Lenovo supercomputer ofCentraleSupélec and ENS Paris Saclay) considering its preliminary results and all CBCT imagesof the imaging phantom will be reinserted in the DCNN for network validation and training. TheCBCT images will be corrected by the trained DCNN and will be analyzed subjectively by fourobservers to indicate the presence of vertical root fracture and root perforation with a 5-pointscale. And, additionally, the images will be analyzed objectively by one observer, that will obtainmeasurements of the signal-to-noise ratio and gray value homogeneity of a homogeneous area.All the images will be evaluated in the CBCT software's OsiriX MD v.7.5.1 and e-Vol DX andwill be posteriorly compared. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

DE LIMA, JOAO PEDRO; MAZZI-CHAVES, JARDEL FRANCISCO; DE SOUSA-NETO, MANOEL DAMIAO; CANDEMIL, AMANDA PELEGRIN. Could Low-dose Cone-beam CT be Used for Endodontic Intraoperative Diagnosis?. JOURNAL OF ENDODONTICS, v. 49, n. 9, p. 8-pg., 2023-09-12. (21/01623-8, 22/07081-5, 22/02664-2)