Advances in Artificial Intelligence to Diagnose Otitis Media: State of the Art Review.

OBJECTIVE: Otitis media (OM) is a model disease for developing, validating, and implementing artificial intelligence (AI) techniques. We aim to review the state of the art applications of AI used to diagnose OM in pediatric and adult populations. DATA SOURCES: Several comprehensive databases were searched to identify all articles that applied AI technologies to diagnose OM. REVIEW METHODS: Relevant articles from January 2010 through May 2021 were identified by title and abstract. Articles were excluded if they did not discuss AI in conjunction with diagnosing OM. References of included studies and relevant review articles were cross-referenced to identify any additional studies. CONCLUSION: Title and abstract screening resulted in full-text retrieval of 40 articles that met initial screening parameters. Of this total, secondary review articles (n = 7) and commentary-based articles (n = 2) were removed, as were articles that did not specifically discuss AI and OM diagnosis (n = 5), leaving 25 articles for review. Applications of AI technologies specific to diagnosing OM included machine learning and natural language processing (n = 23) and prototype approaches (n = 2). IMPLICATIONS FOR PRACTICE: This review emphasizes the utility of AI techniques to automate and aid in diagnosing OM. Although these techniques are still in the development and testing stages, AI has the potential to improve the practice of otolaryngologists and primary care clinicians by increasing the efficiency and accuracy of diagnoses.

Convolutional Neural Network-Based Clinical Predictors of Oral Dysplasia: Class Activation Map Analysis of Deep Learning Results.

Oral cancer/oral squamous cell carcinoma is among the top ten most common cancers globally, with over 500,000 new cases and 350,000 associated deaths every year worldwide. There is a critical need for objective, novel technologies that facilitate early, accurate diagnosis. For this purpose, we have developed a method to classify images as "suspicious" and "normal" by performing transfer learning on Inception-ResNet-V2 and generated automated heat maps to highlight the region of the images most likely to be involved in decision making. We have tested the developed method's feasibility on two independent datasets of clinical photographic images of 30 and 24 patients from the UK and Brazil, respectively. Both 10-fold cross-validation and leave-one-patient-out validation methods were performed to test the system, achieving accuracies of 73.6% (±19%) and 90.9% (±12%), F1-scores of 97.9% and 87.2%, and precision values of 95.4% and 99.3% at recall values of 100.0% and 81.1% on these two respective cohorts. This study presents several novel findings and approaches, namely the development and validation of our methods on two datasets collected in different countries showing that using patches instead of the whole lesion image leads to better performance and analyzing which regions of the images are predictive of the classes using class activation map analysis.

Cloud-Based Federated Learning Implementation Across Medical Centers.

PURPOSE: Building well-performing machine learning (ML) models in health care has always been exigent because of the data-sharing concerns, yet ML approaches often require larger training samples than is afforded by one institution. This paper explores several federated learning implementations by applying them in both a simulated environment and an actual implementation using electronic health record data from two academic medical centers on a Microsoft Azure Cloud Databricks platform. MATERIALS AND METHODS: Using two separate cloud tenants, ML models were created, trained, and exchanged from one institution to another via a GitHub repository. Federated learning processes were applied to both artificial neural networks (ANNs) and logistic regression (LR) models on the horizontal data sets that are varying in count and availability. Incremental and cyclic federated learning models have been tested in simulation and real environments. RESULTS: The cyclically trained ANN showed a 3% increase in performance, a significant improvement across most attempts (P < .05). Single weight neural network models showed improvement in some cases. However, LR models did not show much improvement after federated learning processes. The specific process that improved the performance differed based on the ML model and how federated learning was implemented. Moreover, we have confirmed that the order of the institutions during the training did influence the overall performance increase. CONCLUSION: Unlike previous studies, our work has shown the implementation and effectiveness of federated learning processes beyond simulation. Additionally, we have identified different federated learning models that have achieved statistically significant performances. More work is needed to achieve effective federated learning processes in biomedicine, while preserving the security and privacy of the data.

Digital Otoscopy Videos Versus Composite Images: A Reader Study to Compare the Accuracy of ENT Physicians.

OBJECTIVES/HYPOTHESIS: With the increasing emphasis on developing effective telemedicine approaches in Otolaryngology, this study explored whether a single composite image stitched from a digital otoscopy video provides acceptable diagnostic information to make an accurate diagnosis, as compared with that provided by the full video. STUDY DESIGN: Diagnostic survey analysis. METHODS: Five Ear, Nose, and Throat (ENT) physicians reviewed the same set of 78 digital otoscope eardrum videos from four eardrum conditions: normal, effusion, retraction, and tympanosclerosis, along with the composite images generated by a SelectStitch method that selectively uses video frames with computer-assisted selection, as well as a Stitch method that incorporates all the video frames. Participants provided a diagnosis for each item along with a rating of diagnostic confidence. Diagnostic accuracy for each pathology of SelectStitch was compared with accuracy when reviewing the entire video clip and when reviewing the Stitch image. RESULTS: There were no significant differences in diagnostic accuracy for physicians reviewing SelectStitch images and full video clips, but both provided better diagnostic accuracy than Stitch images. The inter-reader agreement was moderate. CONCLUSIONS: Equal to using full video clips, composite images of eardrums generated by SelectStitch provided sufficient information for ENTs to make the correct diagnoses for most pathologies. These findings suggest that use of a composite eardrum image may be sufficient for telemedicine approaches to ear diagnosis, eliminating the need for storage and transmission of large video files, along with future applications for improved documentation in electronic medical record systems, patient/family counseling, and clinical training. LEVEL OF EVIDENCE: 3 Laryngoscope, 131:E1668-E1676, 2021.

Ros-NET: A deep convolutional neural network for automatic identification of rosacea lesions.

BACKGROUND: Rosacea is one of the most common cutaneous disorder characterized primarily by facial flushing, erythema, papules, pustules, telangiectases, and nasal swelling. Diagnosis of rosacea is principally done by a physical examination and a consistent patient history. However, qualitative human assessment is often subjective and suffers from a relatively high intra- and inter-observer variability in evaluating patient outcomes. MATERIALS AND METHODS: To overcome these problems, we propose a quantitative and reproducible computer-aided diagnosis system, Ros-NET, which integrates information from different image scales and resolutions in order to identify rosacea lesions. This involves adaption of Inception-ResNet-v2 and ResNet-101 to extract rosacea features from facial images. Additionally, we propose to refine the detection results by means of facial-landmarks-based zones (ie, anthropometric landmarks) as regions of interest (ROI), which focus on typical areas of rosacea occurrence on a face. RESULTS: Using a leave-one-patient-out cross-validation scheme, the weighted average Dice coefficients, in percentages, across all patients (N = 41) with 256 × 256 image patches are 89.8 ± 2.6% and 87.8 ± 2.4% with Inception-ResNet-v2 and ResNet-101, respectively. CONCLUSION: The findings from this study support that pre-trained networks trained via transfer learning can be beneficial in identifying rosacea lesions. Our future work will involve expanding the work to a larger database of cases with varying degrees of disease characteristics.