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.

Computer Vision Foundation Models in Endoscopy: Proof of Concept in Oropharyngeal Cancer.

Objectives: To evaluate the performance of vision transformer-derived image embeddings for distinguishing between normal and neoplastic tissues in the oropharynx and to investigate the potential of computer vision (CV) foundation models in medical imaging.

Methods: Computational study using endoscopic frames with a focus on the application of a self-supervised vision transformer model (DINOv2) for tissue classification. High-definition endoscopic images were used to extract image patches that were then normalized and processed using the DINOv2 model to obtain embeddings.

Loss of p53-DREAM-mediated repression of cell cycle genes as a driver of lymph node metastasis in head and neck cancer.

Background: The prognosis for patients with head and neck cancer (HNC) is poor and has improved little in recent decades, partially due to lack of therapeutic options. To identify effective therapeutic targets, we sought to identify molecular pathways that drive metastasis and HNC progression, through large-scale systematic analyses of transcriptomic data.

Methods: We performed meta-analysis across 29 gene expression studies including 2074 primary HNC biopsies to identify genes and transcriptional pathways associated with survival and lymph node metastasis (LNM).

Generating in silico CODEX from a small set of immunofluorescence markers.

Multiplex immunofluorescence (mIF) assays multiple protein biomarkers on a single tissue section. Recently, high-plex CODEX (co-detection by indexing) systems enable simultaneous imaging of 40+ protein biomarkers, unlocking more detailed molecular phenotyping, leading to richer insights into cellular interactions and disease. However, high-plex data can be slower and more costly to collect, limiting its applications, especially in clinical settings.

Using deep learning to identify the recurrent laryngeal nerve during thyroidectomy.

Surgeons must visually distinguish soft-tissues, such as nerves, from surrounding anatomy to prevent complications and optimize patient outcomes. An accurate nerve segmentation and analysis tool could provide useful insight for surgical decision-making. Here, we present an end-to-end, automatic deep learning computer vision algorithm to segment and measure nerves.

Landscape of innate lymphoid cells in human head and neck cancer reveals divergent NK cell states in the tumor microenvironment.

Natural killer (NK) cells comprise one subset of the innate lymphoid cell (ILC) family. Despite reported antitumor functions of NK cells, their tangible contribution to tumor control in humans remains controversial. This is due to incomplete understanding of the NK cell states within the tumor microenvironment (TME).

A Next-Generation Single-Port Robotic Surgical System for Transoral Robotic Surgery: Results From Prospective Nonrandomized Clinical Trials.

Importance: Transoral endoscopic head and neck surgery now plays an important role in the multidisciplinary management of oropharyngeal carcinoma. Previous generations of robotic surgical systems used a multiport system with a rigid stereo-endoscope and 2 wristed instruments that facilitated transoral robotic surgery.

Objective: To evaluate a new single-port robotic surgical system in head and neck surgery prospectively through concurrent nonrandomized clinical trials.

Cost comparison of treatment for oropharyngeal carcinoma.

Objectives/hypothesis: Based on current guidelines, surgical and nonsurgical therapies are viable frontline treatment for patients with locoregional oropharyngeal carcinoma (OPC). We sought to compare financial parameters between chemoradiation and transoral robotic surgery (TORS) in this patient population.

Study Design: Case-control study.