Výsledky vyhledávání - "Walji, Muhammad"

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Artificial Intelligence in Extracting Diagnostic Data from Dental Records

Autor: Chuang, Yao-Shun, Lee, Chun-Teh, Tokede, Oluwabunmi, Lin, Guo-Hao, Brandon, Ryan, Tran, Trung Duong, Jiang, Xiaoqian, Walji, Muhammad F.

This research addresses the issue of missing structured data in dental records by extracting diagnostic information from unstructured text. The updated periodontology classification system's complexity has increased incomplete or missing structured d

Externí odkaz: http://arxiv.org/abs/2407.21050

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Use GPT-J Prompt Generation with RoBERTa for NER Models on Diagnosis Extraction of Periodontal Diagnosis from Electronic Dental Records

Autor: Chuang, Yao-Shun, Jiang, Xiaoqian, Lee, Chun-Teh, Brandon, Ryan, Tran, Duong, Tokede, Oluwabunmi, Walji, Muhammad F.

This study explored the usability of prompt generation on named entity recognition (NER) tasks and the performance in different settings of the prompt. The prompt generation by GPT-J models was utilized to directly test the gold standard as well as t

Externí odkaz: http://arxiv.org/abs/2311.10810

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Extracting periodontitis diagnosis in clinical notes with RoBERTa and regular expression

Autor: Chuang, Yao-Shun, Lee, Chun-Teh, Brandon, Ryan, Tran, Trung Duong, Tokede, Oluwabunmi, Walji, Muhammad F., Jiang, Xiaoqian

This study aimed to utilize text processing and natural language processing (NLP) models to mine clinical notes for the diagnosis of periodontitis and to evaluate the performance of a named entity recognition (NER) model on different regular expressi

Externí odkaz: http://arxiv.org/abs/2311.10809

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Dental CLAIRES: Contrastive LAnguage Image REtrieval Search for Dental Research

Autor: Kabir, Tanjida, Chen, Luyao, Walji, Muhammad F, Giancardo, Luca, Jiang, Xiaoqian, Shams, Shayan

Publikováno v: AMIA 2023 Informatics Summit

Learning about diagnostic features and related clinical information from dental radiographs is important for dental research. However, the lack of expert-annotated data and convenient search tools poses challenges. Our primary objective is to design

Externí odkaz: http://arxiv.org/abs/2306.15651

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Akademický článek

The burden of diagnostic error in dentistry: A study on periodontal disease misclassification

Autor: Tokede, Bunmi, Yansane, Alfa, Brandon, Ryan, Lin, Guo-Hao, Lee, Chun-Teh, White, Joel, Jiang, Xiaoqian, Lee, Eric, Alsaffar, Alaa, Walji, Muhammad, Kalenderian, Elsbeth

Publikováno v: In Journal of Dentistry September 2024 148

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An End-to-end Entangled Segmentation and Classification Convolutional Neural Network for Periodontitis Stage Grading from Periapical Radiographic Images

Autor: Kabir, Tanjida, Lee, Chun-Teh, Nelson, Jiman, Sheng, Sally, Meng, Hsiu-Wan, Chen, Luyao, Walji, Muhammad F, Jiang, Xioaqian, Shams, Shayan

Periodontitis is a biofilm-related chronic inflammatory disease characterized by gingivitis and bone loss in the teeth area. Approximately 61 million adults over 30 suffer from periodontitis (42.2%), with 7.8% having severe periodontitis in the Unite

Externí odkaz: http://arxiv.org/abs/2109.13120

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Use of the Deep Learning Approach to Measure Alveolar Bone Level

Autor: Lee, Chun-Teh, Kabir, Tanjida, Nelson, Jiman, Sheng, Sally, Meng, Hsiu-Wan, Van Dyke, Thomas E., Walji, Muhammad F., Jiang, Xiaoqian, Shams, Shayan

Abstract: Aim: The goal was to use a Deep Convolutional Neural Network to measure the radiographic alveolar bone level to aid periodontal diagnosis. Material and methods: A Deep Learning (DL) model was developed by integrating three segmentation netw

Externí odkaz: http://arxiv.org/abs/2109.12115

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