Microbial Composition of the Laryngotracheal Region: A Systematic Review.

INTRODUCTION: Microbiome research has predominantly focused on the oral cavity and oropharynx's role in disease, while the upper airway, specifically the larynx and trachea, has been relatively overlooked. Examining the microbial communities in these regions can shed light on how dysbiosis influences diseases and their management. This review evaluates laryngotracheal microbial compositions in both healthy and diseased patients. METHODS: We conducted a systematic review in EMBASE, MEDLINE, and Cochrane Central databases, yielding 1383 studies in the initial search. Inclusion criteria involved participants aged over 18 years and the use of next-generation 16s ribosomal sequencing methods. RESULTS: We included 10 studies-seven focused on larynx sequencing and four on trachea sequencing (one investigated both sites). In a healthy larynx, diverse species such as Streptococcus, Cloacibacterium, Prevotella, and Helicobacter were found. Benign laryngeal diseases exhibited reduced microbial diversity, mainly dominated by Streptococcus. Subglottic stenosis patients showed diminished diversity in both idiopathic and iatrogenic scars. Laryngeal squamous cell carcinoma displayed increased diversity, primarily featuring Fusobacterium. Among non-respiratory-compromised surgery patients, the tracheal microbiome was more diverse in diabetics and those later developing lower respiratory infections. Pneumonia patients exhibited an abundance of Prevotella and Streptococcus, linked to an increased 28-day survival rate, while Streptococcus and Haemophilus abundance correlated with successful extubation. CONCLUSIONS: The laryngotracheal region hosts a unique microbial community influenced by both benign and malignant conditions. Many lesions remain unexplored, underscoring the need for future studies encompassing diverse laryngotracheal conditions. Clinical trials assessing microbiome modifications may unveil novel therapeutic avenues. LEVEL OF EVIDENCE: NA Laryngoscope, 2024.

The Use of Deep Learning Software in the Detection of Voice Disorders: A Systematic Review.

OBJECTIVE: To summarize the use of deep learning in the detection of voice disorders using acoustic and laryngoscopic input, compare specific neural networks in terms of accuracy, and assess their effectiveness compared to expert clinical visual examination. DATA SOURCES: Embase, MEDLINE, and Cochrane Central. REVIEW METHODS: Databases were screened through November 11, 2023 for relevant studies. The inclusion criteria required studies to utilize a specified deep learning method, use laryngoscopy or acoustic input, and measure accuracy of binary classification between healthy patients and those with voice disorders. RESULTS: Thirty-four studies met the inclusion criteria, with 18 focusing on voice analysis, 15 on imaging analysis, and 1 both. Across the 18 acoustic studies, 21 programs were used for identification of organic and functional voice disorders. These technologies included 10 convolutional neural networks (CNNs), 6 multilayer perceptrons (MLPs), and 5 other neural networks. The binary classification systems yielded a mean accuracy of 89.0% overall, including 93.7% for MLP programs and 84.5% for CNNs. Among the 15 imaging analysis studies, a total of 23 programs were utilized, resulting in a mean accuracy of 91.3%. Specifically, the twenty CNNs achieved a mean accuracy of 92.6% compared to 83.0% for the 3 MLPs. CONCLUSION: Deep learning models were shown to be highly accurate in the detection of voice pathology, with CNNs most effective for assessing laryngoscopy images and MLPs most effective for assessing acoustic input. While deep learning methods outperformed expert clinical exam in limited comparisons, further studies integrating external validation are necessary.

Incidental Tracheocele as an Unusual Presentation of Pneumomediastinum in the Trauma Setting.

BACKGROUND Tracheocele are rarely encountered air cysts formed due to tracheal wall outpouching through a weak vantage point. The majority are acquired in the adult population and are associated with conditions that weaken the tracheal wall. Most tracheoceles are diagnosed incidentally since many are asymptomatic or present with nonspecific symptoms. Multidetector computed tomography (MDCT) scans are the most common imaging modality for diagnosis of silent tracheal cysts. Tracheocele have been very rarely documented in the trauma setting; therefore, in the setting of multiple body trauma diagnosis can be challenging. CASE REPORT We report a case of an acquired tracheocele after a high-impact blunt thoracic trauma with an admission diagnosis of suspected tracheal perforation. MDCT of the neck and chest demonstrated an irregularly multicystic-shaped air collection at the right posterolateral trachea upon evaluation. Flexible laryngoscopy and bronchoscopy results were unremarkable. CONCLUSIONS Tracheocele are rare and asymptomatic pseudo-diverticulum of the tracheal wall. Many are diagnosed incidentally with imaging studies for other conditions. Nevertheless, in the trauma setting its diagnosis can be challenging and misleading. Consideration of conditions such as tracheocele is important to prevent any unwarranted treatment modalities.