{"doi":"10.1109/embc44109.2020.9176672","title":"Malocclusion Classification on 3D Cone-Beam CT Craniofacial Images Using Multi-Channel Deep Learning Models","abstract":"Analyzing and interpreting cone-beam computed tomography (CBCT) images is a complicated and often time-consuming process. In this study, we present two different architectures of multi-channel deep learning (DL) models: \"Ensemble\" and \"Synchronized multi-channel\", to automatically identify and classify skeletal malocclusions from 3D CBCT craniofacial images. These multi-channel models combine three individual single-channel base models using a voting scheme and a two-step learning process, respectively, to simultaneously extract and learn a visual representation from three different directional views of 2D images generated from a single 3D CBCT image. We also employ a visualization method called \"Class-selective Relevance Mapping\" (CRM) to explain the learned behavior of our DL models by localizing and highlighting a discriminative area within an input image. Our multi-channel models achieve significantly better performance overall (accuracy exceeding 93%), compared to single-channel DL models that only take one specific directional view of 2D projected image as an input. In addition, CRM visually demonstrates that a DL model based on the sagittal-left view of 2D images outperforms those based on other directional 2D images.Clinical Relevance- the proposed method aims at assisting orthodontist to determine the best treatment path for the patient be it orthodontic or surgical treatment or a combination of both.","journal":"PubMed","year":2020,"id":119096,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":28,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9611,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":554769,"name":"Dharitri Misra","orcid":null,"position":1,"is_corresponding":false},{"id":553424,"name":"Laritza Rodriguez","orcid":"0000-0001-6894-7960","position":2,"is_corresponding":false},{"id":553425,"name":"Michael Gill","orcid":"0000-0002-9657-1451","position":3,"is_corresponding":false},{"id":365088,"name":"Denise K. Liberton","orcid":"0000-0002-2885-8864","position":4,"is_corresponding":false},{"id":365085,"name":"Konstantinia Almpani","orcid":"0000-0001-7558-6796","position":5,"is_corresponding":false},{"id":365092,"name":"Janice S. Lee","orcid":"0000-0001-5164-5958","position":6,"is_corresponding":false},{"id":75954,"name":"Sameer Antani","orcid":"0000-0002-0040-1387","position":7,"is_corresponding":false},{"id":465626,"name":"Incheol Kim","orcid":"0000-0002-5754-133X","position":0,"is_corresponding":true}],"reference_count":18,"raw_metadata":null,"created_at":"2026-07-18T23:14:08.313144Z","pmid":"33018225","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}