Examining Diagnostic Errors in the Field of Otorhinolaryngology within the Challenging Landscape of Limited-Resource Healthcare.

Diagnostic accuracy is vital in otorhinolaryngology for effective patient care, yet diagnostic mismatches between non-otorhinolaryngology clinicians and ENT specialists can occur. However, studies investigating such mismatches in low-resource healthcare environments are limited. This study aims to analyze diagnostic mismatches in otorhinolaryngology within a low-resource healthcare environment. A publicly available dataset assessing diagnostic outcomes from non-otorhinolaryngology clinicians and ENT specialists was analyzed. The dataset included demographic characteristics, referral diagnoses, and final ENT specialist diagnoses. Descriptive statistics and appropriate statistical tests were employed to assess the prevalence of diagnostic mismatches and associated factors. The analysis comprised 1544 cases. The prevalence of diagnostic mismatches between non-otorhinolaryngology clinicians and ENT specialists was 67.4%. Certain specific ENT diseases demonstrated higher frequencies of diagnostic mismatches. Factors such as mismatch in the diagnosis and compliance of patient were found to influence the occurrence of diagnostic mismatches. This study highlights the presence of diagnostic mismatches in otorhinolaryngology within a low-resource healthcare environment. The prevalence of these mismatches underscores the need for improved diagnostic practices in such settings. Factors contributing to diagnostic mismatches should be further explored to develop strategies for enhancing diagnostic accuracy and reducing diagnostic errors in otorhinolaryngology.

Clinical acceptance and dosimetric impact of automatically delineated elective target and organs at risk for head and neck MR-Linac patients.

Background: MR-Linac allows for daily online treatment adaptation to the observed geometry of tumor targets and organs at risk (OARs). Manual delineation for head and neck cancer (HNC) patients takes 45-75 minutes, making it unsuitable for online adaptive radiotherapy. This study aims to clinically and dosimetrically validate an in-house developed algorithm which automatically delineates the elective target volume and OARs for HNC patients in under a minute. Methods: Auto-contours were generated by an in-house model with 2D U-Net architecture trained and tested on 52 MRI scans via leave-one-out cross-validation. A randomized selection of 684 automated and manual contours (split half-and-half) was presented to an oncologist to perform a blind test and determine the clinical acceptability. The dosimetric impact was investigated for 13 patients evaluating the differences in dosage for all structures. Results: Automated contours were generated in 8 seconds per MRI scan. The blind test concluded that 114 (33%) of auto-contours required adjustments with 85 only minor and 15 (4.4%) of manual contours required adjustments with 12 only minor. Dosimetric analysis showed negligible dosimetric differences between clinically acceptable structures and structures requiring minor changes. The Dice Similarity coefficients for the auto-contours ranged from 0.66 ± 0.11 to 0.88 ± 0.06 across all structures. Conclusion: Majority of auto-contours were clinically acceptable and could be used without any adjustments. Majority of structures requiring minor adjustments did not lead to significant dosimetric differences, hence manual adjustments were needed only for structures requiring major changes, which takes no longer than 10 minutes per patient.

Prognosticating Acute Ischemic Stroke and Estimating the Feasibility of Mapping Stroke Volume to the Functional Outcomes Using Diffusion-Weighted Images: A Systematic Review Protocol.

Introduction: Diffusion-weighted image or DWI is commonly used to provide valuable and diverse information on acute stroke in tertiary care hospitals. DWI is a sensitive and accurate method for identifying the infarct core and can expose the area of cerebral infarction within a few hours of onset. This systematic review is planned to evaluate the measurement of stroke volume on DWI and correlate it with functional outcomes (modified ranking scale). Method: We have adhered to the PRISMA-P checklist to report this systematic review protocol. PubMed, Web of Science, Scopus, and TRIP (Turning Research into Practice) databases will be searched. Two independent reviewers will screen the records, extract data, and critically appraise the studies. A checklist for critical appraisal will be applied for data abstraction, and data extraction will be done using predictive modeling for systematic reviews. The risk of bias will be measured by the Prediction Model Risk of Bias Assessment Tool (PROBAST). The meta-analysis will be considered only if included studies have adequate data, and STATA statistical package version 13.1 will be used for performing a meta-analysis. A narrative synthesis will be performed if meta-analysis is not possible. Ethics and Dissemination: As this review will focus on secondary information, there is no ethical consideration required. We will disseminate our findings by publishing our analysis in a peer-reviewed journal. Protocol Registration: In Prospective Register of Systematic Reviews (CRD42019141840).

Utility of adding Radiomics to clinical features in predicting the outcomes of radiotherapy for head and neck cancer using machine learning.

BACKGROUND: Radiomics involves the extraction of quantitative information from annotated Computed-Tomography (CT) images, and has been used to predict outcomes in Head and Neck Squamous Cell Carcinoma (HNSCC). Subjecting combined Radiomics and Clinical features to Machine Learning (ML) could offer better predictions of clinical outcomes. This study is a comparative performance analysis of ML models with Clinical, Radiomics, and Clinico-Radiomic datasets for predicting four outcomes of HNSCC treated with Curative Radiation Therapy (RT): Distant Metastases, Locoregional Recurrence, New Primary, and Residual Disease. METHODOLOGY: The study used retrospective data of 311 HNSCC patients treated with radiotherapy between 2013-2018 at our centre. Binary prediction models were developed for the four outcomes with Clinical-only, Clinico-Radiomic, and Radiomics-only datasets, using three different ML classification algorithms namely, Random Forest (RF), Kernel Support Vector Machine (KSVM), and XGBoost. The best-performing ML algorithms of the three dataset groups was then compared. RESULTS: The Clinico-Radiomic dataset using KSVM classifier provided the best prediction. Predicted mean testing accuracy for Distant Metastases, Locoregional Recurrence, New Primary, and Residual Disease was 97%, 72%, 99%, and 96%, respectively. The mean area under the receiver operating curve (AUC) was calculated and displayed for all the models using three dataset groups. CONCLUSION: Clinico-Radiomic dataset improved the predictive ability of ML models over clinical features alone, while models built using Radiomics performed poorly. Radiomics data could therefore effectively supplement clinical data in predicting outcomes.

Noncontact Quantification of Topography of Anterior Corneal Surface and Bowman's Layer With High-Speed OCT.

PURPOSE: To quantify keratometry and wavefront aberration of the anterior corneal surface and epithelium-Bowman's layer interface using anterior segment optical coherence tomography (OCT). METHODS: Twenty-five normal eyes and 25 eyes with keratoconus were retrospectively analyzed. The anterior corneal edge and epithelium-Bowman's layer interface were segmented from 12 distortion-corrected OCT B-scans. Axial tangential curvatures and wavefront aberration were calculated by ray tracing and 6th order Zernike analyses. All eyes underwent simultaneous imaging with Pentacam (Oculus Optikgeräte GmbH, Wetzlar, Germany). The Pentacam elevation data were used for aberration analyses using the same ray-tracing method. The paired t test was used to compare the variables. RESULTS: In normal eyes, mean steep axis and maximum keratometry of OCT of the anterior corneal surface and epithelium-Bowman's layer interface were significantly greater than the same of the Pentacam anterior corneal surface (P < .05). Mean root mean square of higher order aberrations of the OCT surfaces was greater than the same of the Pentacam surface by a factor of 4. In eyes with keratoconus, mean steep axis and maximum keratometry of the OCT epithelium-Bowman's layer interface was the greatest (P < .05). Mean root mean square of the higher order aberrations and vertical coma of the OCT epithelium-Bowman's layer interface was the greatest (P < .05). In general, the aberrations of the OCT epithelium-Bowman's layer interface were significantly greater than those of the Pentacam anterior corneal surface. CONCLUSIONS: A noncontact method to quantify the topography and aberrations of corneal surfaces with OCT was presented. OCT measurements yielded greater curvature and aberrations than Pentacam in both normal and keratoconic eyes. [J Refract Surg. 2017;33(5):330-336.].