ArticleOral radiology2021
Automatic detection of cervical lymph nodes in patients with oral squamous cell carcinoma using a deep learning technique: a preliminary study.
Article in Oral radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.
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Who cites it
27 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep learning in oral cancer- a systematic review.BMC oral health · 2024Pooled it
- Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma-A Systematic Review.Frontiers in oral health · 2021Pooled it
- Automated FDG uptake/PET-CT fused scan diagnosis of various lymph node tumors using object detection AI techniques.Scientific reports · 2026Article
- Oral Cancer Diagnosis Using an Optimized InceptionV3 Model Powered by the Aquila Metaheuristic Algorithm.Asian Pacific journal of cancer prevention : APJCP · 2026Article
- Automated Lymph Node Localization and Segmentation in Patients with Head and Neck Cancer: Opportunities and Limitations of Using a Generic AI Model.Diagnostics (Basel, Switzerland) · 2026Article
- Advancements in Image-Based Artificial Intelligence in the Diagnosis and Treatment of Head and Neck Squamous Cell Carcinoma: A Narrative Review.International journal of general medicine · 2026Review
- Determination of the oral carcinoma and sarcoma in contrast enhanced CT images using deep convolutional neural networks.Scientific reports · 2025Article
- A Deep Learning-Based Detection and Segmentation System for Multimodal Ultrasound Images in the Evaluation of Superficial Lymph Node Metastases.Journal of clinical medicine · 2025Article
- Novel Preprocessing-Based Sequence for Comparative MR Cervical Lymph Node Segmentation.Journal of clinical medicine · 2025Article
- Automatic cervical lymph nodes detection and segmentation in heterogeneous computed tomography images using deep transfer learning.Scientific reports · 2025Article
- Assessing the Reporting Quality of Machine Learning Algorithms in Head and Neck Oncology.The Laryngoscope · 2025Article
- Deep learning-based object detection algorithms in medical imaging: Systematic review.Heliyon · 2025Review
- Mapping the Use of Artificial Intelligence-Based Image Analysis for Clinical Decision-Making in Dentistry: A Scoping Review.Clinical and experimental dental research · 2024Article
- Metastatic Lymph Node Detection on Ultrasound Images Using YOLOv7 in Patients with Head and Neck Squamous Cell Carcinoma.Cancers · 2024Article
- A deep learning and radiomics fusion model based on contrast-enhanced computer tomography improves preoperative identification of cervical lymph node metastasis of oral squamous cell carcinoma.Clinical oral investigations · 2023Article
- Deep learning and artificial intelligence in dental diagnostic imaging.The Japanese dental science review · 2023Review
- Deep Learning Techniques and Imaging in Otorhinolaryngology-A State-of-the-Art Review.Journal of clinical medicine · 2023Review
- Artificial Intelligence-Based Diagnosis of Oral Lichen Planus Using Deep Convolutional Neural Networks.European journal of dentistry · 2023Article
- Automated localization and segmentation of cervical lymph nodes on contrast-enhanced CT using a 3D foveal fully convolutional neural network.European radiology experimental · 2023Article
- Effect of deep transfer learning with a different kind of lesion on classification performance of pre-trained model: Verification with radiolucent lesions on panoramic radiographs.Imaging science in dentistry · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo apply a deep learning object detection technique to CT images for detecting cervical lymph nodes metastasis in patients with oral cancers, and to clarify the detection performance.
methodsOne hundred and fifty-nine metastatic and 517 non-metastatic lymph nodes on 365 CT images in 56 patients with oral squamous cell carcinoma were examined. The images were arbitrarily assigned to training, validation, and testing datasets. Using the neural network, 'DetectNet' for object detection, the training procedure was conducted for 1000 epochs. Testing image datasets were applied to the learning model, and the detection performance was calculated.
resultsThe learning curve indicated that the recall (sensitivity) for detecting metastatic and non-metastatic lymph nodes reached 90% and 80%, respectively, while the model performance recall by applying the test dataset was 73.0% and 52.5%, respectively. The recall for detecting level IB and Level II metastatic lymph nodes was relatively high.
conclusionsA system that has the potential to automatically detect cervical lymph nodes was constructed.
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