ArticleSensors (Basel, Switzerland)2020
Healthcare Professional in the Loop (HPIL): Classification of Standard and Oral Cancer-Causing Anomalous Regions of Oral Cavity Using Textural Analysis Technique in Autofluorescence Imaging.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic performance of artificial intelligence in detecting oral potentially malignant disorders and oral cancer using medical diagnostic imaging: a systematic review and meta-analysis.Frontiers in oral health · 2024Pooled it
- A Web-Based Prediction Model for Cancer-Specific Survival of Middle-Aged Patients With Non-metastatic Renal Cell Carcinoma: A Population-Based Study.Frontiers in public health · 2022Trial
- A Web-Based Prediction Model for Cancer-Specific Survival of Elderly Patients With Clear Cell Renal Cell Carcinoma: A Population-Based Study.Frontiers in public health · 2021Trial
- A scoping review of human-AI collaboration patterns and task divisions in healthcare applications.NPJ digital medicine · 2026Article
- Artificial intelligence and its application in early oral cancer screening: a systematic review.Frontiers in oncology · 2026Review
- Review
- Mapping the Use of Artificial Intelligence-Based Image Analysis for Clinical Decision-Making in Dentistry: A Scoping Review.Clinical and experimental dental research · 2024Article
- Anatomy-Specific Classification Model Using Label-Free FLIm to Aid Intraoperative Surgical Guidance of Head and Neck Cancer.IEEE transactions on bio-medical engineering · 2023Article
- Long-term participant retention and engagement patterns in an app and wearable-based multinational remote digital depression study.NPJ digital medicine · 2023Article
- AI-enabled radiologist in the loop: novel AI-based framework to augment radiologist performance for COVID-19 chest CT medical image annotation and classification from pneumonia.Neural computing & applications · 2023Article
- Classification of Non-Functional Requirements From IoT Oriented Healthcare Requirement Document.Frontiers in public health · 2022Article
- Breast Tumor Detection and Classification in Mammogram Images Using Modified YOLOv5 Network.Computational and mathematical methods in medicine · 2022Article
- A Nomogram for Predicting Cancer-Specific Survival in Children With Wilms Tumor: A Study Based on SEER Database and External Validation in China.Frontiers in public health · 2022Article
- Article
- A Study of the Recent Trends of Immunology: Key Challenges, Domains, Applications, Datasets, and Future Directions.Sensors (Basel, Switzerland) · 2021Review
- Role of Artificial Intelligence in the Early Diagnosis of Oral Cancer. A Scoping Review.Cancers · 2021Article
- Metallothioneins and Megalin Expression Profiling in Premalignant and Malignant Oral Squamous Epithelial Lesions.Cancers · 2021Article
- Harvesting social media sentiment analysis to enhance stock market prediction using deep learning.PeerJ. Computer science · 2021Article
- Development and Application of a Vehicle-Mounted Soil Texture Detector.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Oral mucosal lesions (OML) and oral potentially malignant disorders (OPMDs) have been identified as having the potential to transform into oral squamous cell carcinoma (OSCC). This research focuses on the human-in-the-loop-system named Healthcare Professionals in the Loop (HPIL) to support diagnosis through an advanced machine learning procedure. HPIL is a novel system approach based on the textural pattern of OML and OPMDs (anomalous regions) to differentiate them from standard regions of the oral cavity by using autofluorescence imaging. An innovative method based on pre-processing, e.g., the Deriche-Canny edge detector and circular Hough transform (CHT); a post-processing textural analysis approach using the gray-level co-occurrence matrix (GLCM); and a feature selection algorithm (linear discriminant analysis (LDA)), followed by k-nearest neighbor (KNN) to classify OPMDs and the standard region, is proposed in this paper. The accuracy, sensitivity, and specificity in differentiating between standard and anomalous regions of the oral cavity are 83%, 85%, and 84%, respectively. The performance evaluation was plotted through the receiver operating characteristics of periodontist diagnosis with the HPIL system and without the system. This method of classifying OML and OPMD areas may help the dental specialist to identify anomalous regions for performing their biopsies more efficiently to predict the histological diagnosis of epithelial dysplasia.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.