Real-Time Laryngeal Cancer Boundaries Delineation on White Light and Narrow-Band Imaging Laryngoscopy with Deep Learning

Democratizing cancer detection: artificial intelligence-enhanced endoscopy could address global disparities in head and neck cancer outcomes

INTRODUCTION

This article explores the potential role of artificial intelligence (AI) in enhancing the early detection and diagnosis of head and neck squamous cell carcinoma (HNSCC).

DISCUSSION

The latest data reflecting the growing disparity in HNSCC incidence and mortality across different regions and socioeconomic groups are reviewed, stressing the importance of early detection in improving outcomes. The potential role of AI in improving the accuracy and consistency of endoscopic procedures, particularly in resource-limited settings with limited access to specialized healthcare is discussed. By analyzing current technologies and case studies, the review highlights how AI can democratize access to early cancer detection, improve survival rates, and reduce the financial burden on patients. The integration of AI into healthcare systems can lead to significant cost savings and enhanced accessibility, particularly in underserved populations, where HNSCC is projected to rise dramatically in the coming decades.

Binary Classification of Laryngeal Images Utilising ResNet-50 CNN Architecture

In India, laryngeal cancer is a significant health concern, underlining the critical need for early detection methods. This study introduces a novel approach to classify laryngeal lesions into nine morphological categories; due to data scarcity for all the nine classes, the data is divided into cancer and non-cancer classes, including both non-cancerous and Squamous Cell Carcinoma (SCC), by analysing endoscopy images with advanced convolutional neural networks, deep learning, and image processing techniques. A dataset of 1978 endoscopy images from 960 patients at a tertiary care center in Lucknow, between May 2015 and December 2023, was utilised for this purpose. These images, captured using an Olympus CV-170 processor and annotated via the CVAT tool, were processed to highlight Regions of Interest (ROI) for detailed examination. The dataset was split, with 90% for training/validation and 10% for testing. A total of 197 images out of 1978 were selected for testing, which included 43 cancerous and 154 non-cancerous images. For the feature extraction, ResNet50 was utilised. The model's evaluation through the Receiver Operating Characteristic (ROC) curve demonstrated high effectiveness, with areas of 0.95, 0.98, and 0.93 for combined, NBI-only, and WL-only datasets, respectively. The accuracy rates were notably high across all datasets, highlighting the potential of this model to significantly aid in the early detection and classification of laryngeal cancer. In India, where the incidence of head and neck cancer is high and there is a lack of both advanced instruments and expertise in Narrow Band Imaging (NBI), this model could be instrumental in the early detection of laryngopharyngeal cancer. Level of Evidence: 2 C.

Taming vision transformers for clinical laryngoscopy assessment

OBJECTIVE

Laryngoscopy, essential for diagnosing laryngeal cancer (LCA), faces challenges due to high inter-observer variability and the reliance on endoscopist expertise. Distinguishing precancerous from early-stage cancerous lesions is particularly challenging, even for experienced practitioners, given their similar appearances. This study aims to enhance laryngoscopic image analysis to improve early screening/detection of cancer or precancerous conditions.

METHODS

We propose MedFormer, a laryngeal cancer classification method based on the Vision Transformer (ViT). To address data scarcity, MedFormer employs a customized transfer learning approach that leverages the representational power of pre-trained transformers. This method enables robust out-of-domain generalization by fine-tuning a minimal set of additional parameters.

RESULTS

MedFormer exhibits sensitivity-specificity values of 98%-89% for identifying precancerous lesions (leukoplakia) and 89%-97% for detecting cancer, surpassing CNN counterparts significantly. Additionally, when compared to the two selected ViT-based models, MedFormer also demonstrates superior performance. It also outperforms physician visual evaluations (PVE) in certain scenarios and matches PVE performance in all cases. Visualizations using class activation maps (CAM) and deformable patches demonstrate MedFormer's interpretability, aiding clinicians in understanding the model's predictions.

CONCLUSION

We highlight the potential of visual transformers in clinical laryngoscopic assessments, presenting MedFormer as an effective method for the early detection of laryngeal cancer.

Current Status and Future Directions of Research on Artificial Intelligence in Nasopharyngolaryngoscopy

BACKGROUND

The nasopharyngolaryngoscopy (NPL) has emerged as a valuable tool for detecting early cases of head and neck cancers. However, misdiagnoses and missed diagnoses are still common phenomena. The expertise of examining physicians often serves as the primary limiting factor, leading to issues such as incomplete visualization, imprecise identification, and unclear vision. Over recent years, the application of artificial intelligence (AI) in medical imaging, particularly in the realm of gastrointestinal endoscopy, has instigated revolutionary changes in site quality control, lesion identification, and report generation. However, there remains a lack of standardized guidelines for the proper application of NPL across various countries.

SUMMARY

In this paper, we set our sights on reviewing the current clinical applications and summarizing the primary shortcomings of NPL. In addition, we encapsulate the progress of AI application within gastrointestinal endoscopy and NPL. Drawing from real-world clinical practice, we propose future directions and prospects for AI research in NPL. We firmly believe that the pace of clinical application of AI in NPL will accelerate significantly in the near future.

KEY MESSAGES

Incomplete examination coverage, failure to detect and diagnose lesions, and poor image quality happens in the current use of NPL. Currently, NPL examinations lack third-party supervision and quality control. AI application has achieved great advancements in gastrointestinal endoscopy concerning endoscopic quality control, lesion identification, and standardized reporting. While AI-related research in NPL is still in its nascent stages, it shows substantial potential for clinical application and endoscopic training. The interaction of AI into NPL examinations is potential and inevitable in the era of big data.

Computer Vision and Videomics in Otolaryngology-Head and Neck Surgery: Bridging the Gap Between Clinical Needs and the Promise of Artificial Intelligence

This article discusses the role of computer vision in otolaryngology, particularly through endoscopy and surgery. It covers recent applications of artificial intelligence (AI) in nonradiologic imaging within otolaryngology, noting the benefits and challenges, such as improving diagnostic accuracy and optimizing therapeutic outcomes, while also pointing out the necessity for enhanced data curation and standardized research methodologies to advance clinical applications. Technical aspects are also covered, providing a detailed view of the progression from manual feature extraction to more complex AI models, including convolutional neural networks and vision transformers and their potential application in clinical settings.

Applying Deep Learning with Convolutional Neural Networks to Laryngoscopic Imaging for Automated Segmentation and Classification of Vocal Cord Leukoplakia

Objectives: Vocal cord leukoplakia is clinically described as a white plaque or patch on the vocal cords observed during macroscopic examination, which does not take into account histological features or prognosis. A clinical challenge in managing vocal cord leukoplakia is to assess the potential malignant transformation of the lesion. This study aims to investigate the potential of deep learning (DL) for the simultaneous segmentation and classification of vocal cord leukoplakia using narrow band imaging (NBI) and white light imaging (WLI). The primary objective is to assess the model's accuracy in detecting and classifying lesions, comparing its performance in WLI and NBI. Methods: We applied DL to segment and classify NBI and WLI of vocal cord leukoplakia, and used pathological diagnosis as the gold standard. Results: The DL model autonomously detected lesions with an average intersection-over-union (IoU) >70%. In classification tasks, the model differentiated between lesions in the surgical group with a sensitivity of 93% and a specificity of 94% for WLI, and a sensitivity of 99% and a specificity of 97% for NBI. In addition, the model achieved a mean average precision of 81% in WLI and 92% in NBI, with an IoU threshold >0.5. Conclusions: The model proposed by us is helpful in assisting in accurate diagnosis of vocal cord leukoplakia from NBI and WLI.

An automated approach for real-time informative frames classification in laryngeal endoscopy using deep learning

PURPOSE

Informative image selection in laryngoscopy has the potential for improving automatic data extraction alone, for selective data storage and a faster review process, or in combination with other artificial intelligence (AI) detection or diagnosis models. This paper aims to demonstrate the feasibility of AI in providing automatic informative laryngoscopy frame selection also capable of working in real-time providing visual feedback to guide the otolaryngologist during the examination.

METHODS

Several deep learning models were trained and tested on an internal dataset (n = 5147 images) and then tested on an external test set (n = 646 images) composed of both white light and narrow band images. Four videos were used to assess the real-time performance of the best-performing model.

RESULTS

ResNet-50, pre-trained with the pretext strategy, reached a precision = 95% vs. 97%, recall = 97% vs, 89%, and the F1-score = 96% vs. 93% on the internal and external test set respectively (p = 0.062). The four testing videos are provided in the supplemental materials.

CONCLUSION

The deep learning model demonstrated excellent performance in identifying diagnostically relevant frames within laryngoscopic videos. With its solid accuracy and real-time capabilities, the system is promising for its development in a clinical setting, either autonomously for objective quality control or in conjunction with other algorithms within a comprehensive AI toolset aimed at enhancing tumor detection and diagnosis.

Exploring the landscape of AI-assisted decision-making in head and neck cancer treatment: a comparative analysis of NCCN guidelines and ChatGPT responses

PURPOSE

Recent breakthroughs in natural language processing and machine learning, exemplified by ChatGPT, have spurred a paradigm shift in healthcare. Released by OpenAI in November 2022, ChatGPT rapidly gained global attention. Trained on massive text datasets, this large language model holds immense potential to revolutionize healthcare. However, existing literature often overlooks the need for rigorous validation and real-world applicability.

METHODS

This head-to-head comparative study assesses ChatGPT's capabilities in providing therapeutic recommendations for head and neck cancers. Simulating every NCCN Guidelines scenarios. ChatGPT is queried on primary treatments, adjuvant treatment, and follow-up, with responses compared to the NCCN Guidelines. Performance metrics, including sensitivity, specificity, and F1 score, are employed for assessment.

RESULTS

The study includes 68 hypothetical cases and 204 clinical scenarios. ChatGPT exhibits promising capabilities in addressing NCCN-related queries, achieving high sensitivity and overall accuracy across primary treatment, adjuvant treatment, and follow-up. The study's metrics showcase robustness in providing relevant suggestions. However, a few inaccuracies are noted, especially in primary treatment scenarios.

CONCLUSION

Our study highlights the proficiency of ChatGPT in providing treatment suggestions. The model's alignment with the NCCN Guidelines sets the stage for a nuanced exploration of AI's evolving role in oncological decision support. However, challenges related to the interpretability of AI in clinical decision-making and the importance of clinicians understanding the underlying principles of AI models remain unexplored. As AI continues to advance, collaborative efforts between models and medical experts are deemed essential for unlocking new frontiers in personalized cancer care.