ArticleThe Laryngoscope2023
A Deep Learning Model for Classification of Parotid Neoplasms Based on Multimodal Magnetic Resonance Image Sequences.
Article in The Laryngoscope, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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18 citing papers in PubMed, 29 citations in OpenAlex.
- Deep Learning-Assisted MRI for Differentiating Parotid Gland Tumors: Comparison with FNAB and Radiologic Assessment.Diagnostics (Basel, Switzerland) · 2026Article
- A functionally guided fusion Vision Transformer for predicting IDH status in gliomas: a multicenter study with external validation and incomplete multimodal evaluation.Radiologie (Heidelberg, Germany) · 2026Article
- CT-based habitat analysis combined with multi-channel deep learning for predicting early recurrence after pancreatic cancer resection: a multicenter study.Journal of translational medicine · 2026Article
- Could a multimodal fusion model integrating CT radiomics and systemic inflammatory markers improve preoperative risk stratification of parotid masses? A retrospective exploratory study.Frontiers in immunology · 2026Article
- Automated segmentation and diagnosis of parotid tumors using a combined deep learning and radiomics model on T2-weighted MRI: a multi-vendor validation study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Deep learning for differential diagnosis of parotid tumors based on 2.5D magnetic resonance imaging.Annals of medicine · 2025Article
- Evaluation of deepseek, gemini, ChatGPT-4o, and perplexity in responding to salivary gland cancer.BMC oral health · 2025Article
- Multimodal Integration in Health Care: Development With Applications in Disease Management.Journal of medical Internet research · 2025Review
- Deep learning-based classification of parotid gland tumors: integrating dynamic contrast-enhanced MRI for enhanced diagnostic accuracy.BMC medical imaging · 2025Article
- MR Image Fusion-Based Parotid Gland Tumor Detection.Journal of imaging informatics in medicine · 2025Article
- Parotid Gland Segmentation Using Purely Transformer-Based U-Shaped Network and Multimodal MRI.Annals of biomedical engineering · 2024Article
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- Performance of radiomics in the differential diagnosis of parotid tumors: a systematic review.Frontiers in oncology · 2024Review
- End-stage renal disease accompanied by mild cognitive impairment: A study and analysis of trimodal brain network fusion.PloS one · 2024Article
- Deep Learning Techniques and Imaging in Otorhinolaryngology-A State-of-the-Art Review.Journal of clinical medicine · 2023Review
- Salivary gland pathologies: evolution in classification and association with unique genetic alterations.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2023Review
- Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2023Review
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Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
objectiveTo design a deep learning model based on multimodal magnetic resonance image (MRI) sequences for automatic parotid neoplasm classification, and to improve the diagnostic decision-making in clinical settings.
methodsFirst, multimodal MRI sequences were collected from 266 patients with parotid neoplasms, and an artificial intelligence (AI)-based deep learning model was designed from scratch, combining the image classification network of Resnet and the Transformer network of Natural language processing. Second, the effectiveness of the deep learning model was improved through the multi-modality fusion of MRI sequences, and the fusion strategy of various MRI sequences was optimized. In addition, we compared the effectiveness of the model in the parotid neoplasm classification with experienced radiologists.
resultsThe deep learning model delivered reliable outcomes in differentiating benign and malignant parotid neoplasms. The model, which was trained by the fusion of T2-weighted, postcontrast T1-weighted, and diffusion-weighted imaging (b = 1000 s/mm
conclusionAn accurate and efficient AI based classification model was produced to classify parotid neoplasms, resulting from the fusion of multimodal MRI sequences. The effectiveness certainly outperformed the model with single MRI images or single MRI sequences as input, and potentially, experienced radiologists. LEVEL OF EVIDENCE: 3 Laryngoscope, 133:327-335, 2023.
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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.