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035 a| (Sirsi) o1450451692;

035 a| (OCoLC)1450451692;

049 a| EREE;

090 a| RC78.7.D53;

100 1 a| Vilkomir, Katerina,e| author.?| UNAUTHORIZED;

245 10 a| Deep learning-based mandibular molars detection and classification of furcation involvement /c| by Katerina Vilkomir.;

264 1 a| [Greenville, N.C.] :b| [East Carolina University],c| 2024.;

300 a| 1 online resource (98 pages) :b| illustrations (some color);

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

347 a| text fileb| application/pdfc| 9.79 MB2| rda;

538 a| System requirements: Adobe Reader.;

538 a| Mode of access: World Wide Web.;

502 b| M.S.c| East Carolina Universityd| 2024.;

502 a| Presented to the faculty of the Department of Computer Science;

500 a| Advisor: Nic Herndon;

500 a| Title from PDF t.p. (viewed September 9, 2025).;

520 3 a| The present study aims to investigate the potential of deep learning methodologies in enhancing diagnostic accuracy and healthcare outcomes in dentistry. Specifically, the study explores the effectiveness of convolutional neural networks (CNNs) in detecting dental abnormalities and distinguishing between healthy teeth and those exhibiting signs of furcation involvement (FI). The research findings suggest that CNNs outperform traditional machine learning models in classifying dental imaging data, particularly in detecting furcation involvement. This highlights the potential of deep learning algorithms in medical image analysis tasks. The study also presents a developed algorithm utilizing the Faster R-CNN model, demonstrating promising capabilities in accurately detecting individual teeth on radiographs and streamlining diagnostic procedures. The developed analysis tool offers users an interactive interface to select regions of interest and obtain classification results with ease and precision. Overall, this research highlights the valuable role of artificial intelligence in assisting clinicians with early disease detection and treatment planning in dentistry, which can improve patient care and outcomes in dental healthcare.;

504 a| Includes bibliographical references.;

650 0 a| Diagnostic imaging.=| ^A148134;

650 0 a| Deep learning (Machine learning)=| ^A1458889;

650 0 a| Neural networks (Computer science)=| ^A263002;

650 0 a| Teethx| Rootsx| Furcation.=| ^A1484763;

650 0 a| Molars.=| ^A762146;

650 0 a| Medical protocols.=| ^A4514;

650 0 a| Dentistryv| Methods.=| ^A1918;

653 a| Convolutional Neural Networks;

653 a| Medical Image Analysis;

653 a| Machine Learning Models;

653 a| Radiographs;

653 a| Faster R-CNN Model;

653 a| Artificial Intelligence;

653 a| Early Detection;

653 a| Patient Care;

700 1 a| Herndon, Nic,e| degree supervisor.?| UNAUTHORIZED;

710 2 a| East Carolina University.b| Department of Computer Science.?| UNAUTHORIZED;

856 40 z| Access via ScholarShipu| http://hdl.handle.net/10342/13467;

949 o| wjh;

994 a| C0b| ERE;

596 a| 1 4;

998 a| 6462805;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6462805-1001l| JNETm| JOYNERr| Ys| Yt| JNE3ETDu| 7/31/2024x| ETDz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6462805-2001l| HSLELECm| HSLr| Ys| Yt| HEETDu| 7/31/2024x| ETDz| HERESOURCE;

Deep learning-based mandibular molars detection and classification of furcation involvement / by Katerina Vilkomir.

Author/creator	Vilkomir, Katerina author.
Other author	Herndon, Nic, degree supervisor.
Other author	East Carolina University. Department of Computer Science.
Format	Theses and dissertations
Publication	[Greenville, N.C.] : [East Carolina University], 2024.
Description	1 online resource (98 pages) : illustrations (some color)
Supplemental Content	Access via ScholarShip
Subjects	Diagnostic imaging. Deep learning (Machine learning) Neural networks (Computer science) Teeth--Roots. --Furcation. Molars. Medical protocols. Dentistry--Methods.

Summary	The present study aims to investigate the potential of deep learning methodologies in enhancing diagnostic accuracy and healthcare outcomes in dentistry. Specifically, the study explores the effectiveness of convolutional neural networks (CNNs) in detecting dental abnormalities and distinguishing between healthy teeth and those exhibiting signs of furcation involvement (FI). The research findings suggest that CNNs outperform traditional machine learning models in classifying dental imaging data, particularly in detecting furcation involvement. This highlights the potential of deep learning algorithms in medical image analysis tasks. The study also presents a developed algorithm utilizing the Faster R-CNN model, demonstrating promising capabilities in accurately detecting individual teeth on radiographs and streamlining diagnostic procedures. The developed analysis tool offers users an interactive interface to select regions of interest and obtain classification results with ease and precision. Overall, this research highlights the valuable role of artificial intelligence in assisting clinicians with early disease detection and treatment planning in dentistry, which can improve patient care and outcomes in dental healthcare.
General note	Advisor: Nic Herndon
General note	Title from PDF t.p. (viewed September 9, 2025).
Dissertation note	M.S. East Carolina University 2024.
Dissertation note	Presented to the faculty of the Department of Computer Science
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.

Deep learning-based mandibular molars detection and classification of furcation involvement / by Katerina Vilkomir.

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