Artificial Intelligence in Head and Neck Imaging

Pham, Nancy; Ju, Connie; Kong, Tracie; Mukherji, Suresh K.

doi:10.1053/j.sult.2022.02.006

Cited by 8 publications

(8 citation statements)

References 39 publications

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“…In recent years, the application of AI in healthcare has been touted to use natural language processing (NLP), which is a subfield of AI for differential diagnosis, self-triage, or self-treatment in the form of symptoms and clinical sign checkers [ 33 ]. Recent trends have shown these AI-assisted symptom checkers being integrated as a free web-based (such as the Isabel Symptom Checker) [ 34 ] or AI-powered chatbot system [ 33 , 35 , 36 ]. Therefore, necessary regulations are needed to provide clear guidance for the misuse or unauthorized use of AI.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence in Head and Neck Cancer: A Systematic Review of Systematic Reviews

Mäkitie

Alabi

et al. 2023

Adv Ther

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Introduction Several studies have emphasized the potential of artificial intelligence (AI) and its subfields, such as machine learning (ML), as emerging and feasible approaches to optimize patient care in oncology. As a result, clinicians and decision-makers are faced with a plethora of reviews regarding the state of the art of applications of AI for head and neck cancer (HNC) management. This article provides an analysis of systematic reviews on the current status, and of the limitations of the application of AI/ML as adjunctive decision-making tools in HNC management. Methods Electronic databases (PubMed, Medline via Ovid, Scopus, and Web of Science) were searched from inception until November 30, 2022. The study selection, searching and screening processes, inclusion, and exclusion criteria followed the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. A risk of bias assessment was conducted using a tailored and modified version of the Assessment of Systematic Review (AMSTAR-2) tool and quality assessment using the Risk of Bias in Systematic Reviews (ROBIS) guidelines. Results Of the 137 search hits retrieved, 17 fulfilled the inclusion criteria. This analysis of systematic reviews revealed that the application of AI/ML as a decision aid in HNC management can be thematized as follows: (1) detection of precancerous and cancerous lesions within histopathologic slides; (2) prediction of the histopathologic nature of a given lesion from various sources of medical imaging; (3) prognostication; (4) extraction of pathological findings from imaging; and (5) different applications in radiation oncology. In addition, the challenges in implementation of AI/ML models for clinical evaluations include the lack of standardized methodological guidelines for the collection of clinical images, development of these models, reporting of their performance, external validation procedures, and regulatory frameworks. Conclusion At present, there is a paucity of evidence to suggest the adoption of these models in clinical practice due to the aforementioned limitations. Therefore, this manuscript highlights the need for development of standardized guidelines to facilitate the adoption and implementation of these models in the daily clinical practice. In addition, adequately powered, prospective, randomized controlled trials are urgently needed to further assess the potential of AI/ML models in real-world clinical settings for the management of HNC.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence in Head and Neck Cancer: A Systematic Review of Systematic Reviews

Mäkitie

Alabi

et al. 2023

Adv Ther

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“…This quantitative information contained within images represents a wealth of data for analysis using AI. Currently, various reviews exploring the role of AI in head and neck imaging are available [ 24 , 26 , 27 , 28 ]. However, reviews specifically addressing the current role of AI to classify LN in patients with head and neck squamous cell carcinoma are sparse.…”

Section: Discussionmentioning

confidence: 99%

“…A subset of AI, machine learning (ML) allows computes to learn, through examples, without being programmed with explicit rules [ 24 , 25 ]. Artificial neural networks (ANN) are a subset of ML, which operate on similar functional principles to the human brain neural network, with one input layer of neurons, one or more hidden processing layers of neurons, and one output layer of neurons [ 26 ]. Finally, deep learning (DL) is a subset of ANN, which utilizes more than one hidden processing layer of neurons, per definition [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

“…The typical radiomic analysis workflow in the context of cervical LNs in locally-advanced HNSCC patients starts with detection, followed by segmentation and classification based on the extracted shape, intensity, and texture features, before prognostic implications are explored [ 5 ]. Thus, AI and its respective subsets ML, ANN, and DL may be applied to all of these tasks, ultimately aiming at improving the efficacy of the workflow [ 5 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

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Current Applications of Artificial Intelligence to Classify Cervical Lymph Nodes in Patients with Head and Neck Squamous Cell Carcinoma—A Systematic Review

Santer

Kloppenburg

Gottfried

et al. 2022

Cancers

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Locally-advanced head and neck squamous cell carcinoma (HNSCC) is mainly defined by the presence of pathologic cervical lymph nodes (LNs) with or without extracapsular spread (ECS). Current radiologic criteria to classify LNs as non-pathologic, pathologic, or pathologic with ECS are primarily shape-based. However, significantly more quantitative information is contained within imaging modalities. This quantitative information could be exploited for classification of LNs in patients with locally-advanced HNSCC by means of artificial intelligence (AI). Currently, various reviews exploring the role of AI in HNSCC are available. However, reviews specifically addressing the current role of AI to classify LN in HNSCC-patients are sparse. The present work systematically reviews original articles that specifically explore the role of AI to classify LNs in locally-advanced HNSCC applying Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines and the Study Quality Assessment Tool of National Institute of Health (NIH). Between 2001 and 2022, out of 69 studies a total of 13 retrospective, mainly monocentric, studies were identified. The majority of the studies included patients with oropharyngeal and oral cavity (9 and 7 of 13 studies, respectively) HNSCC. Histopathologic findings were defined as reference in 9 of 13 studies. Machine learning was applied in 13 studies, 9 of them applying deep learning. The mean number of included patients was 75 (SD ± 72; range 10–258) and of LNs was 340 (SD ± 268; range 21–791). The mean diagnostic accuracy for the training sets was 86% (SD ± 14%; range: 43–99%) and for testing sets 86% (SD ± 5%; range 76–92%). Consequently, all of the identified studies concluded AI to be a potentially promising diagnostic support tool for LN-classification in HNSCC. However, adequately powered, prospective, and randomized control trials are urgently required to further assess AI’s role in LN-classification in locally-advanced HNSCC.

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“…Artificial intelligence (AI) has the potential to revolutionize head and neck imaging by augmenting image quality and improving its ability to perform clinically relevant tasks such as tumor volume segmentation, tumor characterization, tumor prognostication, treatment response assessment, and the prediction of metastatic lymph node disease [ 154 , 155 ]. Head and neck oncology care is well positioned for the application of imaging AI since treatment is guided by a wealth of information derived from US, CT, and MRI imaging data.…”

Section: Head and Neckmentioning

confidence: 99%

Artificial Intelligence in Neuroradiology: A Review of Current Topics and Competition Challenges

Wagner,

Tilmans,

Peng

et al. 2023

Diagnostics

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There is an expanding body of literature that describes the application of deep learning and other machine learning and artificial intelligence methods with potential relevance to neuroradiology practice. In this article, we performed a literature review to identify recent developments on the topics of artificial intelligence in neuroradiology, with particular emphasis on large datasets and large-scale algorithm assessments, such as those used in imaging AI competition challenges. Numerous applications relevant to ischemic stroke, intracranial hemorrhage, brain tumors, demyelinating disease, and neurodegenerative/neurocognitive disorders were discussed. The potential applications of these methods to spinal fractures, scoliosis grading, head and neck oncology, and vascular imaging were also reviewed. The AI applications examined perform a variety of tasks, including localization, segmentation, longitudinal monitoring, diagnostic classification, and prognostication. While research on this topic is ongoing, several applications have been cleared for clinical use and have the potential to augment the accuracy or efficiency of neuroradiologists.

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Artificial Intelligence in Head and Neck Imaging

Cited by 8 publications

References 39 publications

Artificial Intelligence in Head and Neck Cancer: A Systematic Review of Systematic Reviews

Artificial Intelligence in Head and Neck Cancer: A Systematic Review of Systematic Reviews

Current Applications of Artificial Intelligence to Classify Cervical Lymph Nodes in Patients with Head and Neck Squamous Cell Carcinoma—A Systematic Review

Artificial Intelligence in Neuroradiology: A Review of Current Topics and Competition Challenges

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