Search Results (5)

In the current landscape of hearing rehabilitation, ear mold manufacturing typically involves the injection of silicone into the external ear canal (EEC) of each patient. This invasive procedure poses several risks, including the potential for silicone residue retention and tympanic membrane perforation, which may necessitate surgical intervention. To mitigate these risks, we present the design of a video otoscope that integrates a scanner capable of capturing high-precision, real-time images of the EEC’s geometry. The developed device allows (i) the generation of a 3D CAD model leading to the direct, quick, and low-cost production of customized hearing aids using 3D printing and (ii) the establishment of medical protocols for carrying out diagnoses and monitoring of hearing pathology evolution using methodologies based on Artificial Intelligence. Furthermore, the use of customized hearing aids that allow the application of Rhythmic Auditory Stimulation (RAS) and music therapy enhances audiology as an alternative and innovative way to treat cognitive and degenerative diseases, as well as pathological disorders. Full article

Telemedicine brings several benefits to patients, healthcare providers, and the wider society, including reductions in the need for hospitalizations or readmissions, as well as in overall healthcare costs and the length of inpatient stay. In addition, these services may provide psychological benefits to patients, including excellent satisfaction and medication adherence. The present study aimed to investigate an in-house-developed otorhinolaryngologic remote diagnostic system (mobile app). The basis of the comparison was the incidence between the diagnoses and therapies made by remote diagnosticians and on-site specialists based on static images and videos captured by a smartphone otoscope device. In the study, 103 patients were involved. After registering demographic data, the telemedicine software was evaluated by comparing the matching of physically established diagnoses and/or therapies with remotely established diagnoses and/or therapies. The most remarkable result was in concordance with the diagnoses, with 79 matches identified of the 103 cases examined; the rate of the matching cases was 76.7% (95% CI: 68.5–84.9%). These results support that telemedicine-based otorhinolaryngological remote diagnostics could play a significant role in future healthcare. Full article

Several studies have shown that in recent years incidence of acute otitis media (AOM) has declined worldwide. However, related medical, social, and economic problems for patients, their families, and society remain very high. Better knowledge of potential risk factors for AOM development and more effective preventive interventions, particularly in AOM-prone children, can further reduce disease incidence. However, a more accurate AOM diagnosis seems essential to achieve this goal. Diagnostic uncertainty is common, and to avoid risks related to a disease caused mainly by bacteria, several children without AOM are treated with antibiotics and followed as true AOM cases. The main objective of this manuscript is to discuss the most common difficulties that presently limit accurate AOM diagnosis and the new approaches and technologies that have been proposed to improve disease detection. We showed that misdiagnosis can be dangerous or lead to relevant therapeutic mistakes. The need to improve AOM diagnosis has allowed the identification of a long list of technologies to visualize and evaluate the tympanic membrane and to assess middle-ear effusion. Most of the new instruments, including light field otoscopy, optical coherence tomography, low-coherence interferometry, and Raman spectroscopy, are far from being introduced in clinical practice. Video-otoscopy can be effective, especially when it is used in association with telemedicine, parents’ cooperation, and artificial intelligence. Introduction of otologic telemedicine and use of artificial intelligence among pediatricians and ENT specialists must be strongly promoted in order to reduce mistakes in AOM diagnosis. Full article

The accurate diagnosis of otitis media (OM) and other middle ear and eardrum abnormalities is difficult, even for experienced otologists. In our earlier studies, we developed computer-aided diagnosis systems to improve the diagnostic accuracy. In this study, we investigate a novel approach, called OtoPair, which uses paired eardrum images together rather than using a single eardrum image to classify them as ‘normal’ or ‘abnormal’. This also mimics the way that otologists evaluate ears, because they diagnose eardrum abnormalities by examining both ears. Our approach creates a new feature vector, which is formed with extracted features from a pair of high-resolution otoscope images or images that are captured by digital video-otoscopes. The feature vector has two parts. The first part consists of lookup table-based values created by using deep learning techniques reported in our previous OtoMatch content-based image retrieval system. The second part consists of handcrafted features that are created by recording registration errors between paired eardrums, color-based features, such as histogram of a* and b* component of the L*a*b* color space, and statistical measurements of these color channels. The extracted features are concatenated to form a single feature vector, which is then classified by a tree bagger classifier. A total of 150-pair (300-single) of eardrum images, which are either the same category (normal-normal and abnormal-abnormal) or different category (normal-abnormal and abnormal-normal) pairs, are used to perform several experiments. The proposed approach increases the accuracy from 78.7% (±0.1%) to 85.8% (±0.2%) on a three-fold cross-validation method. These are promising results with a limited number of eardrum pairs to demonstrate the feasibility of using a pair of eardrum images instead of single eardrum images to improve the diagnostic accuracy. Full article

Background and Objective: the aim of this study is to develop and validate an automated image segmentation-based frame selection and stitching framework to create enhanced composite images from otoscope videos. The proposed framework, called SelectStitch, is useful for classifying eardrum abnormalities using a single composite image instead of the entire raw otoscope video dataset. Methods: SelectStitch consists of a convolutional neural network (CNN) based semantic segmentation approach to detect the eardrum in each frame of the otoscope video, and a stitching engine to generate a high-quality composite image from the detected eardrum regions. In this study, we utilize two separate datasets: the first one has 36 otoscope videos that were used to train a semantic segmentation model, and the second one, containing 100 videos, which was used to test the proposed method. Cases from both adult and pediatric patients were used in this study. A configuration of 4-levels depth U-Net architecture was trained to automatically find eardrum regions in each otoscope video frame from the first dataset. After the segmentation, we automatically selected meaningful frames from otoscope videos by using a pre-defined threshold, i.e., it should contain at least an eardrum region of 20% of a frame size. We have generated 100 composite images from the test dataset. Three ear, nose, and throat (ENT) specialists (ENT-I, ENT-II, ENT-III) compared in two rounds the composite images produced by SelectStitch against the composite images that were generated by the base processes, i.e., stitching all the frames from the same video data, in terms of their diagnostic capabilities. Results: In the first round of the study, ENT-I, ENT-II, ENT-III graded improvement for 58, 57, and 71 composite images out of 100, respectively, for SelectStitch over the base composite, reflecting greater diagnostic capabilities. In the repeat assessment, these numbers were 56, 56, and 64, respectively. We observed that only 6%, 3%, and 3% of the cases received a lesser score than the base composite images, respectively, for ENT-I, ENT-II, and ENT-III in Round-1, and 4%, 0%, and 2% of the cases in Round-2. Conclusions: We conclude that the frame selection and stitching will increase the probability of detecting a lesion even if it appears in a few frames. Full article