Endoscopic surgical field clarity index: An artificial intelligence-based measure of transnasal endoscopic surgical field quality.

KEY POINTS: Clear visualization during transnasal endoscopic surgery (TNES) is crucial for safe, efficient surgery. The endoscopic surgical field clarity index (ESFCI) is an artificial intelligence-enabled measure of surgical field quality. The ESFCI allows researchers to evaluate interventions to improve visualization during TNES.

New Medical Device and Therapeutic Approvals in Otolaryngology: State of the Art Review of 2022.

Objective: To review new drugs and devices relevant to otolaryngology approved by the Food and Drug Administration (FDA) in 2022. Data Sources: Publicly available FDA data on drugs and devices approved in 2022. Review Methods: A preliminary screen was conducted to identify drugs and devices relevant to otolaryngology. A secondary screen by members of the American Academy of Otolaryngology-Head and Neck Surgery's (AAO-HNS) Medical Devices and Drugs Committee differentiated between minor updates and new approvals. The final list of drugs and devices was sent to members of each subspecialty for review and analysis. Conclusion: A total of 1251 devices and 37 drugs were identified on preliminary screening. Of these, 329 devices and 5 drugs were sent to subspecialists for further review, from which 37 devices and 2 novel drugs were selected for further analysis. The newly approved devices spanned all subspecialties within otolaryngology. Many of the newly approved devices aimed to enhance patient experience, including over-the-counter hearing aids, sleep monitoring devices, and refined CPAP devices. Other advances aimed to improve surgical access, convenience, or comfort in the operating room and clinic. Implications for Practice: Many new devices and drugs are approved each year to improve patient care and care delivery. By staying up to date with these advances, otolaryngologists can leverage new innovations to improve the safety and quality of care. Given the recent approval of these devices, further studies are needed to assess long-term impact within the field of otolaryngology.

A modular surgical simulator for microlaryngoscopy using standard instruments and the carbon dioxide laser.

Objective: Build a microlaryngoscopy surgical simulator for endoscopic laryngeal surgery using standard microsurgical instruments and a CO2 laser. Study design: Anatomical modeling, CAD design and 3D printed manufacturing. Subjects and methods: We created a modular design for a microlaryngoscopy simulator in CAD software. Components include plastic and stainless-steel models of a standard operating laryngoscope and a cassette system for mounting porcine or synthetic models of the vocal folds. All simulator parts, including the metallic laryngoscope model, were manufactured using 3D printing technology. Tumors were simulated in porcine tissue models by injecting a soy protein-based tumor phantom. Residents and faculty in the Louisiana State University otolaryngology department evaluated the system. Each participant performed microlaryngoscopy with laser resection on a porcine larynx and cold instrument procedures on synthetic vocal folds. Participants scored the simulator using a 5-point Likert scale. Results: The microlaryngeal surgical simulator demonstrated in this project is realistic, economical, and easily assembled. We have included 3D printed parts files and detailed assembly instructions that will enable educators interested in surgical simulation to build the device.Participants in the simulator evaluation session felt that the simulator faithfully represented the procedure to resect vocal fold lesions using a CO2 laser. The synthetic model allows the trainee to develop hand-eye coordination while using standard laryngeal instruments. Conclusions: The simulator described herein will enable surgeons to acquire the surgical skills necessary to perform operative microlaryngoscopy prior to operating on live patients.

Machine-learning-assisted spontaneous Raman spectroscopy classification and feature extraction for the diagnosis of human laryngeal cancer.

The early detection of laryngeal cancer significantly increases the survival rates, permits more conservative larynx sparing treatments, and reduces healthcare costs. A non-invasive optical form of biopsy for laryngeal carcinoma can increase the early detection rate, allow for more accurate monitoring of its recurrence, and improve intraoperative margin control. In this study, we evaluated a Raman spectroscopy system for the rapid intraoperative detection of human laryngeal carcinoma. The spectral analysis methods included principal component analysis (PCA), random forest (RF), and one-dimensional (1D) convolutional neural network (CNN) methods. We measured the Raman spectra from 207 normal and 500 tumor sites collected from 10 human laryngeal cancer surgical specimens. Random Forest analysis yielded an overall accuracy of 90.5%, sensitivity of 88.2%, and specificity of 92.8% on average over 10 trials. The 1D CNN demonstrated the highest performance with an accuracy of 96.1%, sensitivity of 95.2%, and specificity of 96.9% on average over 50 trials. In predicting the first three principal components (PCs) of normal and tumor data, both RF and CNN demonstrated high performances, except for the tumor PC2. This is the first study in which CNN-assisted Raman spectroscopy was used to identify human laryngeal cancer tissue with extracted feature weights. The proposed Raman spectroscopy feature extraction approach has not been previously applied to human cancer diagnosis. Raman spectroscopy, as assisted by machine learning (ML) methods, has the potential to serve as an intraoperative, non-invasive tool for the rapid diagnosis of laryngeal cancer and margin detection.

Optical Biopsy: Automated Classification of Airway Endoscopic Findings Using a Convolutional Neural Network.

OBJECTIVES/HYPOTHESIS: Create an autonomous computational system to classify endoscopy findings. STUDY DESIGN: Computational analysis of vocal fold images at an academic, tertiary-care laryngology practice. METHODS: A series of normal and abnormal vocal fold images were obtained from the image database of an academic tertiary care laryngology practice. The benign images included normals, nodules, papilloma, polyps, and webs. A separate set of carcinoma and leukoplakia images comprised a single malignant-premalignant class. All images were classified with their existing labels. Images were randomly withheld from each class for testing. The remaining images were used to train and validate a neural network for classifying vocal fold lesions. Two classifiers were developed. A multiclass system classified the five categories of benign lesions. A separate analysis was performed using a binary classifier trained to distinguish malignant-premalignant from benign lesions. RESULTS: Precision ranged from 71.7% (polyps) to 89.7% (papilloma), and recall ranged from 70.0% (papilloma) to 88.0% (nodules) for the benign classifier. Overall accuracy for the benign classifier was 80.8%. The binary classifier correctly identified 92.0% of the malignant-premalignant lesions with an overall accuracy of 93.0%. CONCLUSIONS: Autonomous classification of endoscopic images with artificial intelligence technology is possible. Better network implementations and larger datasets will continue to improve classifier accuracy. A clinically useful optical cancer screening system may require a multimodality approach that incorporates nonvisual spectra. LEVEL OF EVIDENCE: NA Laryngoscope, 132:S1-S8, 2022.

Detection of pancreatic cancer by convolutional-neural-network-assisted spontaneous Raman spectroscopy with critical feature visualization.

Pancreatic cancer is the deadliest cancer type with a five-year survival rate of less than 9%. Detection of tumor margins plays an essential role in the success of surgical resection. However, histopathological assessment is time-consuming, expensive, and labor-intensive. We constructed a lab-designed, hand-held Raman spectroscopic system that could enable intraoperative tissue diagnosis using convolutional neural network (CNN) models to efficiently distinguish between cancerous and normal pancreatic tissue. To our best knowledge, this is the first reported effort to diagnose pancreatic cancer by CNN-aided spontaneous Raman scattering with a lab-developed system designed for intraoperative applications. Classification based on the original one-dimensional (1D) Raman, two-dimensional (2D) Raman images, and the first principal component (PC1) from the principal component analysis on the 2D image, could all achieve high performance: the testing sensitivity, specificity, and accuracy were over 95%, and the area under the curve approached 0.99. Although CNN models often show great success in classification, it has always been challenging to visualize the CNN features in these models, which has never been achieved in the Raman spectroscopy application in cancer diagnosis. By studying individual Raman regions and by extracting and visualizing CNN features from max-pooling layers, we identified critical Raman peaks that could aid in the classification of cancerous and noncancerous tissues. 2D Raman PC1 yielded more critical peaks for pancreatic cancer identification than that of 1D Raman, as the Raman intensity was amplified by 2D Raman PC1. To our best knowledge, the feature visualization was achieved for the first time in the field of CNN-aided spontaneous Raman spectroscopy for cancer diagnosis. Based on these CNN feature peaks and their frequency at specific wavenumbers, pancreatic cancerous tissue was found to contain more biochemical components related to the protein contents (particularly collagen), whereas normal pancreatic tissue was found to contain more lipids and nucleic acid (particularly deoxyribonucleic acid/ribonucleic acid). Overall, the CNN model in combination with Raman spectroscopy could serve as a useful tool for the extraction of key features that can help differentiate pancreatic cancer from a normal pancreas.

Classification of tracheal stenosis in children based on computational aerodynamics.

Tracheal stenosis is a health condition in which local narrowing of the upper trachea can cause breathing difficulties and increased incidence of infection, among other symptoms. Occurring most commonly due to intubation of infants, tracheal stenosis often requires corrective surgery. It is challenging to determine the most effective surgical strategy for a given patient as current clinical methods used to assess tracheal stenosis are simplistic and subjective, and are not rigorously based on aerodynamic considerations. This paper summarizes a non-invasive approach based on computational fluid dynamics (CFD) and medical imaging to establish relationships between trachea anatomy and inspiration performance. Though patient-specific CFD analysis has gained recent popularity, an objective of this study is to computationally formulate dimensionless analytical correlations between anatomy and performance that are applicable to any member of a class of patients and that can be interpreted within the context of the Myer-Cotton stenotic airway classification system. These correlations can provide aerodynamics-based insight for the development of more robust stenosis evaluation methods and may allow for time-efficient assessment of corrective surgical strategies.

A Surgical Simulator for Tympanostomy Tube Insertion Incorporating Capacitive Sensing Technology to Track Instrument Placement.

We describe a device engineered for realistic simulation of myringotomy and tympanostomy tube insertion that tracks instrument placement and objectively measures operator proficiency. A 3-dimensional computer model of the external ear and cartilaginous external auditory canal was created from a normal maxillofacial computed tomography scan, and models for the bony external auditory canal and tympanic cavity were created with computer-aided design software. Physical models were 3-dimensionally printed from the computer reconstructions. The external auditory canal and tympanic cavity surfaces were coated with conductive material and wired to a capacitive sensor interface. A programmable microcontroller with custom embedded software completed the system. Construct validation was completed by comparing the run times and total sensor contact times of otolaryngology faculty and residents.

Image-Based 3-Dimensional Characterization of Laryngotracheal Stenosis in Children.

OBJECTIVES: Describe a technique for the description and classification of laryngotracheal stenosis in children using 3-dimensional reconstructions of the airway from computed tomography (CT) scans. STUDY DESIGN: Cross-sectional. SETTING: Academic tertiary care children's hospital. SUBJECTS AND METHODS: Three-dimensional models of the subglottic airway lumen were created using CT scans from 54 children undergoing imaging for indications other than airway disease. The base lumen models were deformed in software to simulate subglottic airway segments with 0%, 25%, 50%, and 75% stenoses for each subject. Statistical analysis of the airway geometry was performed using metrics extracted from the lumen centerlines. The centerline analysis was used to develop a system for subglottic stenosis assessment and classification from patient-specific airway imaging. RESULTS: The scaled hydraulic diameter gradient metric derived from intersectional changes in the lumen can be used to accurately classify and quantitate subglottic stenosis in the airway based on CT scan imaging. Classification is most accurate in the clinically relevant 25% to 75% range of stenosis. CONCLUSIONS: Laryngotracheal stenosis is a complex diagnosis requiring an understanding of the airway lumen configuration, anatomical distortions of the airway framework, and alterations of respiratory aerodynamics. Using image-based airway models, we have developed a metric that accurately captures subglottis patency. While not intended to replace endoscopic evaluation and existing staging systems for laryngotracheal stenosis, further development of these techniques will facilitate future studies of upper airway computational fluid dynamics and the clinical evaluation of airway disease.