Deep learning for diagnosis of head and neck cancers through radiographic data: a systematic review and meta-analysis

QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies

Whiting, P. F., Rutjes, A. W. S., Westwood, M. E., Mallett, S., Deeks, J. J., Reitsma, J. B., Leeflang, M. M. G., Sterne, J. A. C., Bossuyt, P. M. M., & the QUADAS-2 Group* (2011) · Annals of Internal Medicine · doi:10.7326/0003-4819-155-8-201110180-00009 · Cited by 14,199 · see citing works

High-performance medicine: the convergence of human and artificial intelligence

Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450 · see citing works

Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies: The PRISMA-DTA Statement

McInnes, M. D. F., Moher, D., Thombs, B. D., McGrath, T. A., Bossuyt, P. M., and the PRISMA-DTA Group, Clifford, T., Cohen, J. F., Deeks, J. J., Gatsonis, C., et al. (2018) · JAMA · doi:10.1001/jama.2017.19163 · Cited by 3,210 · see citing works

Clinically applicable deep learning for diagnosis and referral in retinal disease

De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., Askham, H., Glorot, X., O’Donoghue, B., Visentin, D., et al. (2018) · Nature Medicine · doi:10.1038/s41591-018-0107-6 · Cited by 2,639 · see citing works

Head and neck cancer

Argiris, A. E., Karamouzis, M. V., Raben, D., & Ferris, R. L. (2008) · The Lancet · doi:10.1016/s0140-6736(08)60728-x · Cited by 2,287 · see citing works

Explainable artificial intelligence (XAI) in deep learning-based medical image analysis

van der Velden, B. H. M., Kuijf, H. J., Gilhuijs, K. G. A., & Viergever, M. A. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102470 · Cited by 1,328 · cited 1× · see citing works

Deep Learning for Medical Image Processing: Overview, Challenges and the Future

Razzak, I., Naz, S., & Zaib, A. (2017) · Lecture notes in computational vision and biomechanics · doi:10.1007/978-3-319-65981-7_12 · Cited by 1,305 · see citing works

Predicting cancer outcomes from histology and genomics using convolutional networks

Mobadersany, P., Yousefi, S., Amgad, M., Gutman, D. A., Barnholtz-Sloan, J. S., Velázquez Vega, J. E., Brat, D. J., & Cooper, L. A. D. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1717139115 · Cited by 1,084 · see citing works

Head and neck cancer

Mody, M. D., Rocco, J. W., Yom, S. S., Haddad, R. I., & Saba, N. F. (2021) · The Lancet · doi:10.1016/s0140-6736(21)01550-6 · Cited by 877 · see citing works

Explainable Deep Learning Models in Medical Image Analysis

Singh, A., Sengupta, S., & Lakshminarayanan, V. (2020) · Journal of Imaging · doi:10.3390/jimaging6060052 · Cited by 718 · see citing works

Oral cancer diagnosis and perspectives in India

Borse, V. B., Konwar, A. N., & Buragohain, P. (2020) · Sensors International · doi:10.1016/j.sintl.2020.100046 · Cited by 493 · see citing works

Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images

Sahiner, B., Chan, H., Petrick, N., Wei, D., Helvie, M. A., Adler, D. D., & Goodsitt, M. M. (1996) · IEEE Transactions on Medical Imaging · doi:10.1109/42.538937 · Cited by 420 · see citing works

Computer-assisted medical image classification for early diagnosis of oral cancer employing deep learning algorithm

Jeyaraj, P. R., & Nadar, E. R. S. (2019) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-018-02834-7 · Cited by 366 · see citing works

Deep learning for caries detection: A systematic review

Mohammad‐Rahimi, H., Motamedian, S. R., Rohban, M. H., Krois, J., Uribe, S. E., Mahmoudinia, E., Rokhshad, R., Nadimi, M., & Schwendicke, F. (2022) · Journal of Dentistry · doi:10.1016/j.jdent.2022.104115 · Cited by 321 · see citing works

Improving Oral Cancer Outcomes with Imaging and Artificial Intelligence

İlhan, B., Lin, K., Güneri, P., & Wilder-Smith, P. B. B. (2020) · Journal of Dental Research · doi:10.1177/0022034520902128 · Cited by 209 · see citing works

Pretreatment Identification of Head and Neck Cancer Nodal Metastasis and Extranodal Extension Using Deep Learning Neural Networks

Kann, B. H., Aneja, S., Loganadane, G., Kelly, J. R., Smith, S. J., Decker, R. H., Yu, J. B., Park, H. S.-M., Yarbrough, W. G., Malhotra, A., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-32441-y · Cited by 206 · see citing works

Deep learning in head & neck cancer outcome prediction

Diamant, A., Chatterjee, A., Vallières, M., Shenouda, G., & Seuntjens, J. P. (2019) · Scientific Reports · doi:10.1038/s41598-019-39206-1 · Cited by 202 · see citing works

Contrast-enhanced computed tomography image assessment of cervical lymph node metastasis in patients with oral cancer by using a deep learning system of artificial intelligence

Ariji, Y., Fukuda, M., Kise, Y., Nozawa, M., Yanashita, Y., Fujita, H., Katsumata, A., & Ariji, E. (2018) · Oral Surgery Oral Medicine Oral Pathology and Oral Radiology · doi:10.1016/j.oooo.2018.10.002 · Cited by 180 · see citing works

Automated classification of cells into multiple classes in epithelial tissue of oral squamous cell carcinoma using transfer learning and convolutional neural network

Das, N., Hussain, E., & Mahanta, L. B. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.05.003 · Cited by 168 · see citing works

Deep Learning Algorithm for Auto-Delineation of High-Risk Oropharyngeal Clinical Target Volumes With Built-In Dice Similarity Coefficient Parameter Optimization Function

Cardenas, C. E., McCarroll, R. E., Court, L. E., Elgohari, B., Elhalawani, H. M., Fuller, C. D., Kamal, M. J., Meheissen, M. A. M., Mohamed, A., Rao, A., et al. (2018) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2018.01.114 · Cited by 150 · see citing works

Artificial Intelligence in Radiology—Ethical Considerations

Brady, A. P., & Neri, E. (2020) · Diagnostics · doi:10.3390/diagnostics10040231 · Cited by 150 · see citing works

Multi-Institutional Validation of Deep Learning for Pretreatment Identification of Extranodal Extension in Head and Neck Squamous Cell Carcinoma

Kann, B. H., Hicks, D. F., Payabvash, S., Mahajan, A. K., Du, J., Gupta, V., Park, H. S.-M., Yu, J. B., Yarbrough, W. G., Burtness, B. A., et al. (2019) · Journal of Clinical Oncology · doi:10.1200/jco.19.02031 · Cited by 148 · see citing works

Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions

Tufail, A. B., Ma, Y., Kaabar, M. K. A., Martínez, F., Junejo, A. R., Ullah, I., & Khan, R. (2021) · Computational and Mathematical Methods in Medicine · doi:10.1155/2021/9025470 · Cited by 148 · see citing works

The Effectiveness of Artificial Intelligence in Detection of Oral Cancer

Al-Rawi, N. H., Sultan, A. S., Rajai, B., Shuaeeb, H., Alnajjar, M., Alketbi, M. M., Mohammad, Y., Shetty, S. R., & Mashrah, M. A. (2022) · International Dental Journal · doi:10.1016/j.identj.2022.03.001 · Cited by 143 · see citing works

Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with CT

Lee, J. H., Ha, E. J., & Kim, J. H. (2019) · European Radiology · doi:10.1007/s00330-019-06098-8 · Cited by 129 · see citing works

Artificial intelligence in early diagnosis and prevention of oral cancer

Hegde, S., Ajıla, V., Zhu, W., & Zeng, C. (2022) · Asia-Pacific Journal of Oncology Nursing · doi:10.1016/j.apjon.2022.100133 · Cited by 127 · see citing works

Responsibility beyond design: Physicians’ requirements for ethical medical AI

Sand, M., Durán, J. M., & Jongsma, K. R. (2021) · Bioethics · doi:10.1111/bioe.12887 · Cited by 109 · see citing works

Application of deep learning to the diagnosis of cervical lymph node metastasis from thyroid cancer with CT: external validation and clinical utility for resident training

Lee, J. H., Ha, E. J., Kim, D., Jung, Y. J., Heo, S., Jang, Y.-H., An, S. H., & Lee, K. (2020) · European Radiology · doi:10.1007/s00330-019-06652-4 · Cited by 95 · see citing works

Development of a self-constrained 3D DenseNet model in automatic detection and segmentation of nasopharyngeal carcinoma using magnetic resonance images

Ke, L., Deng, Y., Xia, W., Qiang, M., Chen, X., Liu, K., Jing, B., He, C., Xie, C., Guo, X., et al. (2020) · Oral Oncology · doi:10.1016/j.oraloncology.2020.104862 · Cited by 84 · see citing works

CT evaluation of extranodal extension of cervical lymph node metastases in patients with oral squamous cell carcinoma using deep learning classification

Ariji, Y., Sugita, Y., Nagao, T., Nakayama, A., Fukuda, M., Kise, Y., Nozawa, M., Nishiyama, M., Katumata, A., & Ariji, E. (2019) · Oral Radiology · doi:10.1007/s11282-019-00391-4 · Cited by 82 · see citing works

Automatic Nasopharyngeal Carcinoma Segmentation Using Fully Convolutional Networks with Auxiliary Paths on Dual-Modality PET-CT Images

Zhao, L., Lu, Z., Jiang, J., Zhou, Y., Wu, Y., & Feng, Q. (2019) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-018-00173-0 · Cited by 67 · see citing works

Auto-delineation of oropharyngeal clinical target volumes using 3D convolutional neural networks

Cardenas, C. E., Anderson, B., Aristophanous, M., Yang, J., Rhee, D. J., McCarroll, R. E., Mohamed, A., Kamal, M. J., Elgohari, B., Elhalawani, H. M., et al. (2018) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aae8a9 · Cited by 58 · see citing works

Tumor Segmentation in Contrast-Enhanced Magnetic Resonance Imaging for Nasopharyngeal Carcinoma: Deep Learning with Convolutional Neural Network

Li, Q., Yu-zhen, X., Chen, Z., Liu, D., Feng, S., Law, M. W.-M., Ye, Y., & Huang, B. (2018) · BioMed Research International · doi:10.1155/2018/9128527 · Cited by 56 · see citing works

Virtual Contrast-Enhanced Magnetic Resonance Images Synthesis for Patients With Nasopharyngeal Carcinoma Using Multimodality-Guided Synergistic Neural Network

Li, W., Xiao, H., Li, T., Ren, G., Lam, S., Teng, X., Liu, C., Zhang, J., Lee, F. K.-H., Au, K., et al. (2021) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2021.11.007 · Cited by 56 · see citing works

Evaluation of deep learning-based multiparametric MRI oropharyngeal primary tumor auto-segmentation and investigation of input channel effects: Results from a prospective imaging registry

Wahid, K. A., Ahmed, S., He, R., van Dijk, L. V., Teuwen, J., McDonald, B. A., Salama, V., Mohamed, A., Salzillo, T. C., Dede, C., et al. (2021) · Clinical and Translational Radiation Oncology · doi:10.1016/j.ctro.2021.10.003 · Cited by 55 · see citing works

Nasopharyngeal carcinoma segmentation based on enhanced convolutional neural networks using multi-modal metric learning

Ma, Z., Zhou, S., Wu, X., Zhang, H., Yan, W., Sun, S., & Zhou, J. (2018) · Physics in Medicine and Biology · doi:10.1088/1361-6560/aaf5da · Cited by 54 · see citing works

The role of imaging in head and neck cancer: An overview of different imaging modalities in primary diagnosis and staging of the disease

Vishwanath, V., Jafarieh, S., & Rembielak, A. (2020) · Journal of Contemporary Brachytherapy · doi:10.5114/jcb.2020.100386 · Cited by 53 · see citing works

Convolutional neural network for discriminating nasopharyngeal carcinoma and benign hyperplasia on MRI

Wong, L. M., King, A. D., Ai, Q. Y. H., Lam, W. K., Poon, D. M. C., Ma, B. B. Y., Chan, K. C. A., & Mo, F. K. F. (2020) · European Radiology · doi:10.1007/s00330-020-07451-y · Cited by 52 · see citing works

Automatic detection of cervical lymph nodes in patients with oral squamous cell carcinoma using a deep learning technique: a preliminary study

Ariji, Y., Fukuda, M., Nozawa, M., Kuwada, C., Goto, M., Ishibashi, K., Nakayama, A., Sugita, Y., Nagao, T., & Ariji, E. (2020) · Oral Radiology · doi:10.1007/s11282-020-00449-8 · Cited by 47 · see citing works

Deep learning-based auto-delineation of gross tumour volumes and involved nodes in PET/CT images of head and neck cancer patients

Moe, Y. M., Groendahl, A. R., Tomić, O., Dale, E., Malinen, E., & Futsæther, C. M. (2021) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-020-05125-x · Cited by 47 · see citing works

Deep learning model for tongue cancer diagnosis using endoscopic images

Heo, J., Lim, J. H., Lee, H., Jang, J. Y., Shin, Y. S., Kim, D., Lim, J. Y., Park, Y. M., Koh, Y. W., Ahn, S., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-10287-9 · Cited by 46 · see citing works

Esophageal cancer detection based on classification of gastrointestinal CT images using improved Faster RCNN

Chen, K.-B., Xuan, Y., Lin, A.-J., & Guo, S. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106172 · Cited by 45 · see citing works

Utilizing Artificial Intelligence for Head and Neck Cancer Outcomes Prediction From Imaging

Chinnery, T., Arifin, A. J., Tay, K. Y., Leung, A., Nichols, A. C., Palma, D. A., Mattonen, S. A., & Lang, P. (2020) · Canadian Association of Radiologists Journal · doi:10.1177/0846537120942134 · Cited by 41 · see citing works

3D segmentation of nasopharyngeal carcinoma from CT images using cascade deep learning

Daoud, B., Morooka, K., Kurazume, R., Farhat, L., Mnejja, W., & Daoud, J. (2019) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2019.101644 · Cited by 40 · see citing works

Deep Learning for Differentiating Benign From Malignant Parotid Lesions on MR Images

Xia, X., Feng, B., Wang, J., Hua, Q., Yang, Y., Liang, S., Mou, Y., & Hu, W. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2021.632104 · Cited by 39 · see citing works

Deep learning for automatic tumour segmentation in PET/CT images of patients with head and neck cancersPreprint

Moe, Y. M., Groendahl, A. R., Mulstad, M., Tomić, O., Indahl, U. G., Dale, E., Malinen, E., & Futsæther, C. M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.00841 · Cited by 35 · see citing works

Artificial Intelligence and Radiomics in Head and Neck Cancer Care: Opportunities, Mechanics, and Challenges

van Dijk, L. V., & Fuller, C. D. (2021) · American Society of Clinical Oncology Educational Book · doi:10.1200/edbk_320951 · Cited by 33 · see citing works

Research on the Classification of Benign and Malignant Parotid Tumors Based on Transfer Learning and a Convolutional Neural Network

Zhang, H., Lai, H., Wang, Y., Lv, X., Hong, Y., Peng, J., Zhang, Z., Chen, C., & Chen, C. (2021) · IEEE Access · doi:10.1109/access.2021.3064752 · Cited by 29 · see citing works

Automatic segmentation of head and neck primary tumors on MRI using a multi-view CNN

Schouten, J. P. E., Noteboom, S., Martens, R. M., Mes, S. W., Leemans, C. R., de Graaf, P., & Steenwijk, M. D. (2022) · Cancer Imaging · doi:10.1186/s40644-022-00445-7 · Cited by 29 · see citing works

Enhanced Computational Model for Gravitational Search Optimized Echo State Neural Networks Based Oral Cancer Detection

Al‐Maitah, M., & AlZubi, A. A. (2018) · Journal of Medical Systems · doi:10.1007/s10916-018-1052-0 · Cited by 27 · see citing works

The contrast-enhanced MRI can be substituted by unenhanced MRI in identifying and automatically segmenting primary nasopharyngeal carcinoma with the aid of deep learning models: An exploratory study in large-scale population of endemic area

Deng, Y., Li, C., Lv, X., Xia, W., Shen, L., Jing, B., Li, B., Guo, X., Sun, Y. H., Xie, C., et al. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106702 · Cited by 21 · see citing works

Feature Selection of OMIC Data by Ensemble Swarm Intelligence Based Approaches

Yao, Z., Zhu, G., Too, J., Duan, M., & Wang, Z. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2021.793629 · Cited by 21 · see citing works

The role of neural networks in improving the accuracy of MR spectroscopy for the diagnosis of head and neck squamous cell carcinoma.

Gerstle, R. J., Aylward, S., Kromhout-Schiro, S., & Mukherji, S. K. (2000) · PubMed Central · Cited by 21 · see citing works

Deep learning enables automated localization of the metastatic lymph node for thyroid cancer on 131I post-ablation whole-body planar scans

Kavitha, M. S., Lee, C.-H., Shibudas, K., Kurita, T., & Ahn, B. (2020) · Scientific Reports · doi:10.1038/s41598-020-64455-w · Cited by 19 · see citing works

Computer-aided diagnosis and regional segmentation of nasopharyngeal carcinoma based on multi-modality medical images

Qi, Y., Li, J., Chen, H., Guo, Y., Yin, Y., Gong, G., & Wang, L. (2021) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-021-02351-y · Cited by 18 · see citing works

Continual improvement of nasopharyngeal carcinoma segmentation with less labeling effort

Men, K., Chen, X., Zhu, J., Yang, B., Zhang, Y., Yi, J., & Dai, A. J. (2020) · Physica Medica · doi:10.1016/j.ejmp.2020.11.005 · Cited by 12 · see citing works

Application of artificial neural network algorithm to detection of parathyroid adenoma.

Stefaniak, B., Cholewiński, W., & Tarkowska, A. (2003) · PubMed · Cited by 12 · see citing works

Convolutional Neural Network Intelligent Segmentation Algorithm-Based Magnetic Resonance Imaging in Diagnosis of Nasopharyngeal Carcinoma Foci

Wang, D., Gong, Z., Zhang, Y., & Wang, S. (2021) · Contrast Media & Molecular Imaging · doi:10.1155/2021/2033806 · Cited by 3 · see citing works

Automatic PET volume analysis and classification based on ANN and BIC

Sharif, M. S., Abbod, M., Krill, B., Amira, A., & Zaidi, H. (2011) · IEEE International Symposium on Consumer Electronics (ISCE) · doi:10.1109/isce.2011.5973894 · Cited by 2 · see citing works

The Transformative Role of Artificial Intelligence in Dentistry: A Comprehensive Overview. Part 1: Fundamentals of AI, and its Contemporary Applications in Dentistry

Samaranayake, L., Tuygunov, N., Schwendicke, F., Osathanon, T., Khurshid, Z., Boymuradov, S. A., & Cahyanto, A. (2025) · International Dental Journal · doi:10.1016/j.identj.2025.02.005 · Cited by 240 · see citing works

Deep learning for age estimation from panoramic radiographs: A systematic review and meta-analysis

Rokhshad, R., Nasiri, F., Saberi, N., Shoorgashti, R., Ehsani, S., Nasiri, Z., Azadi, A., & Schwendicke, F. (2025) · Journal of Dentistry · doi:10.1016/j.jdent.2025.105560 · Cited by 33 · see citing works

The role of artificial intelligence in shaping dentistry through advancement in data acquisition, clinical practice, education, and research

Tay, F. R., Loveless, R., & Ravenel, T. D. (2026) · Dental Research · doi:10.1016/j.dtrs.2025.100005 · Cited by 24 · see citing works

Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews

Xu, H., Gong, T., Song, X., Chen, Q., Bao, Q., Yao, W., Xie, M.-M., Li, C., Grzegorzek, M., Shi, Y., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/53567 · Cited by 21 · see citing works

Artificial intelligence-assisted detection of nasopharyngeal carcinoma on endoscopic images: a national, multicentre, model development and validation study

Shi, Y., Li, Z., Wang, L., Wang, H., Liu, X., Gu, D., Chen, X., Liu, X., Gong, W., Jiang, X., et al. (2025) · The Lancet Digital Health · doi:10.1016/j.landig.2025.03.001 · Cited by 15 · see citing works

Deep learning-based breast MRI for predicting axillary lymph node metastasis: a systematic review and meta-analysis

Lee, C.-F., Lin, J., Huang, Y.-L., Chen, S.-T., Chou, C., Chen, D.-R., & Wu, W.-P. (2025) · Cancer Imaging · doi:10.1186/s40644-025-00863-3 · Cited by 14 · see citing works

Deep learning applications in prosthodontics: A systematic review

Rokhshad, R., Khosravi, K., Motie, P., Sadeghi, T. S., Tehrani, A. M., Zarbakhsh, A., & Revilla‐León, M. (2025) · Journal of Prosthetic Dentistry · doi:10.1016/j.prosdent.2025.04.009 · Cited by 14 · see citing works

Predicting prognosis for epithelial ovarian cancer patients receiving bevacizumab treatment with CT-based deep learning

Huang, X., Huang, Y., Liu, K., Zhang, F., Zhu, Z., Xu, K., & Li, P. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00688-6 · Cited by 7 · see citing works

Artificial intelligence in radiology

Elizondo‐Riojas, G., Negreros-Osuna, A. A., Bernal-Ramirez, J. M., & Conde-Castro, B. (2024) · Journal of the Mexican Federation of Radiology and Imaging · doi:10.24875/jmexfri.m24000073 · Cited by 6 · see citing works

Diagnostic Performance of Artificial Intelligence in Detecting and Distinguishing Pancreatic Ductal Adenocarcinoma via Computed Tomography: A Systematic Review and Meta-Analysis

Harandi, H., Gouravani, M., Alikarami, S., Farahani, M. S., Ghavam, M., Mohammadi, S., Salehi, M. A., Reynolds, S., Firouzabadi, F. D., & Huda, F. (2025) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01607-2 · Cited by 5 · see citing works

Predicting alveolar nerve injury and the difficulty level of extraction impacted third molars: a systematic review of deep learning approaches

Salieti, H. A., Qasem, H. M., Alshwayyat, S., Almasri, N., Alshwayyat, M., Aboali, A. A., Alsarayrah, F., Khasawneh, L., & Al‐kurdi, M. A. (2025) · Frontiers in Dental Medicine · doi:10.3389/fdmed.2025.1534406 · Cited by 4 · see citing works

Application and progress of digital techniques in head and neck oncology reconstructive surgery

Zhang, Z., & Ji, T. (2025) · Holistic Integrative Oncology · doi:10.1007/s44178-024-00137-1 · Cited by 3 · see citing works

Determination of the oral carcinoma and sarcoma in contrast enhanced CT images using deep convolutional neural networks

Warin, K., Limprasert, W., Paipongna, T., Chaowchuen, S., & Vicharueang, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-06318-w · Cited by 3 · see citing works

Challenges, Biases, and Limitations in Systematic Reviews and Meta-Analyses: Addressing Pitfalls in Medical Research Methodologies

Bigham, M. (2023) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2023.57554 · Cited by 2 · see citing works

Harnessing Artificial Intelligence in Head and Neck Oncology Practice

Rao, K. N., Kirsch, C., Mahmood, H., & Simon, C. (2025) · Otolaryngologic Clinics of North America · doi:10.1016/j.otc.2025.09.003 · Cited by 2 · see citing works

A Hybrid U-Net and Vision Transformer Framework with Dimensionality Reduction for Automated Detection of Throat Cancer Metastasis

Jayapal, S., & Annamalai, R. (2026) · International Journal of Online and Biomedical Engineering (iJOE) · doi:10.3991/ijoe.v22i08.62322 · Cited by 1 · see citing works

A Scoping Review of Implemented Innovations in Cancer Care: Implications for Pan-Canadian Scaling

Sampalli, T., Tomblin Murphy, G., Peacock, S., Navaratnam, S., Domm, D., & MacKenzie, K. (2026) · Current Oncology · doi:10.3390/curroncol33070395

Challenges, Biases, and Limitations in Systematic Reviews and Meta-Analyses: Addressing Pitfalls inMedical Research Methodologies

Bigham, M. (2023) · International Journal of Current Research in Medical Sciences · doi:10.22192/ijcrms.2023.09.10.005

Deep Learning-Based Tumor Detection in Head and Neck Cancer Using PET Scans

Manjari, P. D., Kumar, D. P., & Jyotsna, G. (2026) · Lecture notes in networks and systems · doi:10.1007/978-981-95-2875-2_20

Diagnostic Performance of Artificial Intelligence Models for Head and Neck Tumours

Verma, K. K. (2026) · Indian Journal of Otolaryngology and Head & Neck Surgery · doi:10.1007/s12070-026-06409-8

Systematic reviews on artificial intelligence applications in dentistry- A bibliometric evaluationPreprint

Issa, J., Ammar, N., Sivaramkrishnan, G., Yuwanati, M. B., & Chaurasia, A. (2026) · Research Square · doi:10.21203/rs.3.rs-8953891/v1

DSR-VGG: A Lightweight Depthwise Separable Residual VGG Architecture for Nasopharyngeal Histopathological Image Classification

Abdullahi, M. K., Mansor, S. B., & Fauzi, M. F. A. (2026) · Diagnostics · doi:10.3390/diagnostics16203406

A National Multicenter Study on Artificial Intelligence-Based Detection of Nasopharyngeal Carcinoma from Endoscopic Images

Eriksen, K., Berg, S., & Dahl, M. (2024) · Journal of Medical Sciences and Interdisciplinary Research · doi:10.51847/8miluhmksq

Imaging Modalities and Deep Learning for Head and Neck Cancer Detection

Sunkara, N. K. M. S., Jyotsna, G., & Bolem, A. B. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-981-96-9720-5_25

Comparative diagnostic accuracy of deep learning and hand-crafted radiomics models for detecting lymph node metastases in head and neck cancers: A meta-analysis.

Rehman, A., Maqbool, S., Khan, I., & Shafiq, S. (2025) · Journal of Clinical Oncology · doi:10.1200/jco.2025.43.16_suppl.e18023

Diagnostic Performance of Artificial Intelligence Models for Head and Neck Tumours

Verma, K. K. (2026) · Indian Journal of Otolaryngology and Head & Neck Surgery · doi:10.1007/s12070-026-06409-8

Application of PET/CT-based deep learning radiomics in head and neck cancer prognosis: a systematic review

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