Laryngeal Cancer Screening During Flexible Video Laryngoscopy Using Large Computer Vision Models.

OBJECTIVE: Develop an artificial intelligence assisted computer vision model to screen for laryngeal cancer during flexible laryngoscopy. METHODS: Using laryngeal images and flexible laryngoscopy video recordings, we developed computer vision models to classify video frames for usability and cancer screening. A separate model segments any identified lesions on the frames. We used these computer vision models to construct a video stream annotation system. This system classifies findings from flexible laryngoscopy as "potentially malignant" or "probably benign" and segments any detected lesions. Additionally, the model provides a confidence level for each classification. RESULTS: The overall accuracy of the flexible laryngoscopy cancer screening model was 92%. For cancer screening, it achieved a sensitivity of 97.7% and a specificity of 76.9%. The segmentation model attained an average precision at a 0.50 intersection-over-union of 0.595. The confidence level for positive screening results can assist clinicians in counseling patients regarding the findings. CONCLUSION: Our model is highly sensitive and adequately specific for laryngeal cancer screening. Segmentation helps endoscopists identify and describe potential lesions. Further optimization is required to enable the model's deployment in clinical settings for real-time annotation during flexible laryngoscopy.

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.

Computational fluid dynamics model of laryngotracheal stenosis and correlation to pulmonary function measures.

3D models of airway lumens were created from CT scans of 19 patients with laryngotracheal stenosis. Computational fluid dynamics (CFD) simulations were completed for each, and results were compared to measured peak inspiratory flow rate, grade of lumen constriction, and measures of airway geometry. Results demonstrate flow resistance and shear stress correlate with degree of lumen constriction and absolute cross-sectional area as well as flow rate. Flow recirculation depends on airway constriction but does not vary with flow rate. Resistance and wall shear stress did not correlate well with functional measures. Flow recirculation did differ between subjects with higher functional measures and subjects with lower functional measures. This analysis provides mathematical models to predict airway resistance, wall shear stress, and flow reversal according lumen constriction and inspiratory flow rate. It suggests aerodynamic factors such as flow recirculation play a role in differences in functional performance between patients with similar airway measures.

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.

Autonomous diagnosis of pediatric cutaneous vascular anomalies using a convolutional neural network.

OBJECTIVES: Design and validate a novel handheld device for the autonomous diagnosis of pediatric vascular anomalies using a convolutional neural network (CNN). STUDY DESIGN: Retrospective, cross-sectional study of medical images. Computer aided design and 3D printed manufacturing. METHODS: We obtained a series of head and neck vascular anomaly images in pediatric patients from the database maintained in a large multidisciplinary vascular anomalies clinic. The database was supplemented with additional images from the internet. Four diagnostic classes were recognized in the dataset - infantile hemangioma, capillary malformation, venous malformation, and arterio-venous malformation. Our group designed and implemented a convolutional neural network to recognize the four classes of vascular anomalies as well as a fifth class consisting of none of the vascular anomalies. The system was based on the Inception-Resnet neural network using transfer learning. For deployment, we designed and built a compact, handheld device including a central processing unit, display subsystems, and control electronics. The device focuses upon and autonomously classifies pediatric vascular lesions. RESULTS: The multiclass system distinguished the diagnostic categories with an overall accuracy of 84%. The inclusion of lesion metadata improved overall accuracy to 94%. Sensitivity ranged from 88% (venous malformation) to 100% (arterio-venous malformation and capillary malformation). CONCLUSIONS: An easily deployed handheld device to autonomously diagnose pediatric skin lesions is feasible. Large training datasets and novel neural network architectures will be required for successful implementation.

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.

Management of haemothoraces in blunt thoracic trauma: study protocol for a randomised controlled trial.

INTRODUCTION: Haemothorax following blunt thoracic trauma is a common source of morbidity and mortality. The optimal management of moderate to large haemothoraces has yet to be defined. Observational data have suggested that expectant management may be an appropriate strategy in stable patients. This study aims to compare the outcomes of patients with haemothoraces following blunt thoracic trauma treated with either chest drainage or expectant management. METHODS AND ANALYSIS: This is a single-centre, dual-arm randomised controlled trial. Patients presenting with a moderate to large sized haemothorax following blunt thoracic trauma will be assessed for eligibility. Eligible patients will then undergo an informed consent process followed by randomisation to either (1) chest drainage (tube thoracostomy) or (2) expectant management. These groups will be compared for the rate of additional thoracic interventions, major thoracic complications, length of stay and mortality. ETHICS AND DISSEMINATION: This study has been approved by the institution's research ethics board and registered with ClinicalTrials.gov. All eligible participants will provide informed consent prior to randomisation. The results of this study may provide guidance in an area where there remains significant variation between clinicians. The results of this study will be published in peer-reviewed journals and presented at national and international conferences. TRIAL REGISTRATION NUMBER: NCT03050502.

Neurocognitive assessment in patients with a minor traumatic brain injury and an abnormal initial CT scan: Can cognitive evaluation assist in identifying patients who require surveillance CT brain imaging?

BACKGROUND: Evidence for repeat computed tomography (CT) in minor traumatic brain injury (mTBI) patients with intracranial pathology is scarce. The aim of this study was to investigate the utility of clinical cognitive assessment (COG) in defining the need for repeat imaging. METHODS: COG performance was compared with findings on subsequent CT, and need for neurosurgery in mTBI patients (GCS 13-15 and positive CT findings). RESULTS: Of 152 patients, 65.8% received a COG (53.0% passed). Patients with passed COG underwent fewer repeat CT (43.4% vs. 78.7%; p = .001) and had shorter LOS (8.7 vs. 19.5; p < .05). Only 1 patient required neurosurgery after a passed COG. The negative predictive value of a normal COG was 90.6% (95%CI = 81.8%-95.4%). CONCLUSION: mTBI patients with an abnormal index CT who pass COG are less likely to undergo repeat CT head, and rarely require neurosurgery. The COG warrants further investigation to determine its role in omitting repeat head CT.

A triple protostar system formed via fragmentation of a gravitationally unstable disk.

Binary and multiple star systems are a frequent outcome of the star formation process and as a result almost half of all stars with masses similar to that of the Sun have at least one companion star. Theoretical studies indicate that there are two main pathways that can operate concurrently to form binary/multiple star systems: large-scale fragmentation of turbulent gas cores and filaments or smaller-scale fragmentation of a massive protostellar disk due to gravitational instability. Observational evidence for turbulent fragmentation on scales of more than 1,000 astronomical units has recently emerged. Previous evidence for disk fragmentation was limited to inferences based on the separations of more-evolved pre-main sequence and protostellar multiple systems. The triple protostar system L1448 IRS3B is an ideal system with which to search for evidence of disk fragmentation as it is in an early phase of the star formation process, it is likely to be less than 150,000 years old and all of the protostars in the system are separated by less than 200 astronomical units. Here we report observations of dust and molecular gas emission that reveal a disk with a spiral structure surrounding the three protostars. Two protostars near the centre of the disk are separated by 61 astronomical units and a tertiary protostar is coincident with a spiral arm in the outer disk at a separation of 183 astronomical units. The inferred mass of the central pair of protostellar objects is approximately one solar mass, while the disk surrounding the three protostars has a total mass of around 0.30 solar masses. The tertiary protostar itself has a minimum mass of about 0.085 solar masses. We demonstrate that the disk around L1448 IRS3B appears susceptible to disk fragmentation at radii between 150 and 320 astronomical units, overlapping with the location of the tertiary protostar. This is consistent with models for a protostellar disk that has recently undergone gravitational instability, spawning one or two companion stars.

Imaging the water snow-line during a protostellar outburst.

A snow-line is the region of a protoplanetary disk at which a major volatile, such as water or carbon monoxide, reaches its condensation temperature. Snow-lines play a crucial role in disk evolution by promoting the rapid growth of ice-covered grains. Signatures of the carbon monoxide snow-line (at temperatures of around 20 kelvin) have recently been imaged in the disks surrounding the pre-main-sequence stars TW Hydra and HD163296 (refs 3, 10), at distances of about 30 astronomical units (au) from the star. But the water snow-line of a protoplanetary disk (at temperatures of more than 100 kelvin) has not hitherto been seen, as it generally lies very close to the star (less than 5 au away for solar-type stars). Water-ice is important because it regulates the efficiency of dust and planetesimal coagulation, and the formation of comets, ice giants and the cores of gas giants. Here we report images at 0.03-arcsec resolution (12 au) of the protoplanetary disk around V883 Ori, a protostar of 1.3 solar masses that is undergoing an outburst in luminosity arising from a temporary increase in the accretion rate. We find an intensity break corresponding to an abrupt change in the optical depth at about 42 au, where the elevated disk temperature approaches the condensation point of water, from which we conclude that the outburst has moved the water snow-line. The spectral behaviour across the snow-line confirms recent model predictions: dust fragmentation and the inhibition of grain growth at higher temperatures results in soaring grain number densities and optical depths. As most planetary systems are expected to experience outbursts caused by accretion during their formation, our results imply that highly dynamical water snow-lines must be considered when developing models of disk evolution and planet formation.

Circumstellar disks of the most vigorously accreting young stars.

Stars may not accumulate their mass steadily, as was previously thought, but in a series of violent events manifesting themselves as sharp stellar brightening. These events can be caused by fragmentation due to gravitational instabilities in massive gaseous disks surrounding young stars, followed by migration of dense gaseous clumps onto the star. Our high-resolution near-infrared imaging has verified the presence of the key associated features, large-scale arms and arcs surrounding four young stellar objects undergoing luminous outbursts. Our hydrodynamics simulations and radiative transfer models show that these observed structures can indeed be explained by strong gravitational instabilities occurring at the beginning of the disk formation phase. The effect of those tempestuous episodes of disk evolution on star and planet formation remains to be understood.

Intranasal Fentanyl Intoxication Leading to Diffuse Alveolar Hemorrhage.

CONTEXT: Increasing rates of opioid abuse, particularly fentanyl, may lead to more presentations of unusual effects of opioid toxicity. Diffuse alveolar hemorrhage is a rare complication of fentanyl overdose. CASE DETAILS: A 45-year-old male presented in hypoxic respiratory failure secondary to diffuse alveolar hemorrhage requiring intubation. Comprehensive drug screening detected fentanyl without exposure to cocaine. Further history upon the patient's recovery revealed exposure to snorted fentanyl powder immediately prior to presentation. DISCUSSION: Diffuse alveolar hemorrhage is a potential, though rare, presentation of opioid intoxication. CONCLUSIONS: Recognition of less common complications of opioid abuse such as diffuse alveolar hemorrhage is important in proper management of overdoses.