Background: The aim of this study was a comprehensive analysis of the incidence of different salivary gland pathologies in the adult population of Poland. Methods: A retrospective analysis of salivary gland pathologies diagnosed in Poland in 2010–2019 based on the National Health Fund (NHF) database was performed. Non-neoplastic diseases, and benign and malignant lesions were identified using ICD-10 codes. Demographic characteristics, incidence rates, and the number of inpatient and outpatient medical services were analyzed. Results: Salivary gland pathologies were diagnosed in 230,589 patients over 10 years (85.5% were non-neoplastic lesions, 11.53% benign and 2.93% malignant neoplasms). Incidence rate for all pathologies was 59.94/100,000. The mean incidence for malignant neoplasms was 1.78, and decreasing trend was observed over the analyzed period. Contrarily, for benign neoplasms (mean incidence—6.91), an increase in numbers was noted annually. The incidence for non-malignant lesions was quite stable (mean: 51.25) over the time. The highest number of medical services per patient concerned malignant neoplasms (on average, two hospital stays, and eleven outpatient consultations). Conclusions: An increase of benign salivary gland tumors, and a decrease of malignant neoplasms was observed during the studied period. The number of medical services related to salivary gland pathologies increased during the period under study.

Background: Early diagnosis of laryngeal lesions is necessary to begin treatment of patients as soon as possible to preserve optimal organ functions. Imaging examinations are often aided by artificial intelligence (AI) to improve quality and facilitate appropriate diagnosis. The aim of this study is to investigate diagnostic utility of AI in laryngeal endoscopy. Methods: Five databases were searched for studies implementing artificial intelligence (AI) enhanced models assessing images of laryngeal lesions taken during laryngeal endoscopy. Outcomes were analyzed in terms of accuracy, sensitivity, and specificity. Results: All 11 studies included presented an overall low risk of bias. The overall accuracy of AI models was very high (from 0.806 to 0.997). The accuracy was significantly higher in studies using a larger database. The pooled sensitivity and specificity for identification of healthy laryngeal tissue were 0.91 and 0.97, respectively. The same values for differentiation between benign and malignant lesions were 0.91 and 0.94, respectively. The comparison of the effectiveness of AI models assessing narrow band imaging and white light endoscopy images revealed no statistically significant differences (p = 0.409 and 0.914). Conclusion: In assessing images of laryngeal lesions, AI demonstrates extraordinarily high accuracy, sensitivity, and specificity.

(1) Background: Malignant tumours of the salivary glands have different clinical and histopathological characteristics. They most commonly involve the parotid gland. Histopathologically, the most common are mucoepidermoid carcinoma (MEC), adenoid cystic carcinoma (AdCC), acinic cell carcinoma (AcCC), adenocarcinoma, carcinoma in pleomorphic adenoma (CPA), and squamous cell carcinoma (SCC). (2) Methods: We analysed 2318 patients with malignant parotid gland tumours reported to the National Cancer Registry (NCR) in Poland over 20 years (1999–2018). The demographic characteristics of patients, clinical factors, and overall survival (OS) were analysed. (3) Results: The average age was 61.33 ± 16.1 years. The majority were males (55%) and urban citizens (64%). High percentage of carcinomas was diagnosed in locoregional (33.7%) and systemic (10.4%) stadium. The most prevalent diagnoses were SCC (33.3%) and adenocarcinoma (19.6%). Surgical resection with adjuvant RT (42.1%) was the most common treatment. The OS analysis showed a median survival time of 5.6 years. The most favorable median OS was found in patients with AcCC (18.30 years), the worst for SCC (1.58 years). (4) Conclusion: AcCC has the best prognosis and SCC the worst. Tumour stadium, treatment, and demographic factors affect prognosis. Improvements in diagnosis and re-evaluation of treatment standards are necessary to enhance the outcome of patients with parotid gland cancers in Poland.

Background: Early and proper diagnosis of laryngeal lesions is necessary to begin treatment of the patient as soon as possible with the possibility of preserve organ functions. Imaging examinations are oft aided by artificial intelligence (AI) to improve quality and facilitate appropriate diagnosis. The aim of the study is to investigate of the diagnostic utility of AI in laryngeal endoscopy. Methods: Five electronic databases (PubMed, Embase, Cochrane, Scopus, Web of Science) were searched for studies published before October 15, 2021 implementing artificial intelligence (AI) enhanced models assessing images of laryngeal lesions taken during laryngeal endoscopy. Outcomes were analyzed in terms of accuracy, sensitivity and specificity. Results: All 13 included studies presented overall low risk of bias. The overall accuracy of AI models was very high (from 0.806 to 0.997) and the number of images used to build and evaluate the models ranged from 120 to 24,667. The accuracy was significantly higher in studies using larger database. The pooled sensitivity and specificity for identification of healthy laryngeal tissue (8 studies) was 0.91 (95% CI: 0.83-0.98) and 0.97 (95% CI: 0.96-0.99), respectively. The same values for differentiation between benign and malignant lesions (7 studies) were 0.91 (95% CI: 0.86-0.96) and 0.95 (95% CI: 0.90-0.99), respectively. The analysis was extended to a comparison of sensitivity and specificity of AI models assessing Narrow Band Imaging (3 studies) and white light endoscopy images (4 studies). The results were similar for both methods, no subgroup effect was revealed (p = 0.406 for sensitivity and p = 0.817 for specificity). Conclusions: In assessing images of laryngeal lesions, AI demonstrates extraordinarily high accuracy, sensitivity, and specificity. AI enhanced diagnostic tools should be introduced into everyday clinical work. The performance of AI diagnoses increases efficacy with the size of the image database when using similar standards for evaluating images. The multicentre cooperation should concentrate on creation of huge database of laryngeal lesions images and implement their sharing, which allows building AI modes with the best performance, based on vast amount of images for learning and testing.

Purpose: The aim of the study was to analyze the impact of the COVID-19 pandemic and the associated change in teaching mode from stationary to distance learning on the severity of voice-related complaints among teachers. Materials and methods: A questionnaire survey of teachers was conducted to assess voice disorders during stationary and remote work using the Vocal Tract Dyscomfort (VTDs) scale and Numeric Rating Scale (NRS), and respondents' subjective feelings were assessed. The demographic and environmental factors related to voice work were examined. Data on sickness absenteeism obtained from the Healthcare Needs Maps 2020 of the Ministry of Health were also analyzed. The statistical analysis of responses was conducted. A p-value below 0.05 was considered statistically significant. Results: 128 teachers participated in the survey. The overall assessment of voice disorders using VTDs and NRS scales did not show statistically significant differences for complaints between stationary and remote work. Detailed analysis revealed more severe voice disorders in teachers working more than 6 months remotely (p = 0.049) and having more than 20 lessons per week (p = 0.012). The subjective assessment confirmed a significantly lower percentage of teachers reporting voice disorders during remote work compared to stationary work (p = 0.043). This resulted in a reduction of sickness absences and a 40% decrease in sick leave related to voice disorders in 2020 compared to 2019. Conclusions: During the remote learning period in the COVID-19 pandemic, teachers reported lower severity of voice disorders and this contributed to a reduction in sickness absences. There were no statistically significant differences in voice-related complaints assessed by the VTDs and NRS scales for either mode of teaching. Several factors affecting the severity of vocal tract disorders were identified - the number of teaching hours per week (> 20) for stationary work and a long period of remote teaching (> 6 months).