Figure - available from: Journal of Big Data
This content is subject to copyright. Terms and conditions apply.
Various types of abnormal mastoids were presented. a Normal mastoid pneumatization on both right and left ear (class 1), b None pneumatization on both sides as yellow arrows indicate that there are no air cells (class 2), c Opacification in Complete pneumatization on the right mastoid which is shown by red arrows, left mastoid is normal, complete pneumatized, and there is no sclerosis on both sides (class 3), d Yellow arrows present none pneumatized parts of the mastoid on both side and the rest parts of the mastoid have opacified which is pointed by red arrows (class 4), e The right mastoid is partially pneumatized. The yellow arrow shows the sclerotic part of the right mastoid, while the left side is normal and completely pneumatized (class 5)
Source publication
Purpose
Mastoid abnormalities show different types of ear illnesses, however inadequacy of experts and low accuracy of diagnostic demand a new approach to detect these abnormalities and reduce human mistakes. The manual analysis of mastoid CT scans is time-consuming and labor-intensive. In this paper the first and robust deep learning-based approac...
Similar publications
p class="abstract"> Background: Chronic otitis media (COM) is common disease prevalent in India. Patients coming to OPD who were clinically diagnosed as chronic otitis media (COM) were evaluated using High resolution computerised tomography (HRCT) temporal bone. HRCT temporal bone is highly useful in knowing the disease involving middle ear cleft a...
Citations
... The training process involved training both networks for 200 epochs with a learning rate of 0.001. Additionally, various optimizers such as Adam (Adaptive moment estimation) [10], Adamax [22], and Stochastic Gradient Descent (SGD) [11] were tested as part of the experimentation. The findings revealed that Adam yielded superior performance compared to the other optimizers. ...
Image change detection in remote sensing is crucial for monitoring environmental changes at different temporal and spatial scales. The primary goal is to identify changed pixels in multi-temporal images accurately. However, challenges such as limited large-scale validation and response latency persist. In this study, we propose an accurate automated deep learning change detection method called "ResNet-Unet" based on multi-spectral NDVI imagery. ResNet-Unet combines UNet and residual networks, while employing deep learning based features for precise change detection. We evaluate the proposed method on low-resolution data from three geographical regions: Colombia, California, and Duluth. In each region, there are 145,161 patches, giving us extensive coverage for our experiments. Our method is validated in three different areas, where we achieve an accuracy of 99.50% and an F1 score of 99.41%.
... The diagnostic process is carried out to facilitate the process of identifying a disease that is difficult to detect [14]. One of the diseases that can be detected with this CT-scan is the MACS in the temporal bone [15]. ...
Mastoiditis occurs due to inflammation that can affect the structure of the mastoid bone. The mastoid bone consists of the mastoid air cell system (MACS) which protects the ear structures and regulates air pressure in the ear and has different sizes and characteristics, making it very difficult to identify precisely. This study aims to identify and find the right MACS size by developing an automatic identification model and obtaining the optimal threshold value in the segmentation process using the extended adaptive threshold (eAT) method. The research dataset uses computed tomography (CT)-scan images of 308 slices of 12 patients indicated for mastoiditis. The results of this study provide identification that has the right MACS accuracy and size. Overall, the optimal segmentation process obtained the smallest threshold value of 57 and the largest threshold value of 63, the smallest MACS size is 4.025 cm2 and the largest is 8.816 cm2 with an accuracy rate of 93.4%. The smaller MACS size indicates inflammation in the mastoid area and these patients require more intensive treatment.
... Some studies used a single AI algorithm, while others combined 2 or more algorithms to solve clinical problems. 25,38,63,74,75,79 The data sources were used to train, validate, and test AI models, including single-and multiple-center databases, 52,81 and public databases. 25,85 Viscaino et al 26 used 22,000 otoscopic images to train a model that classifies 4 middle and external ear conditions: normal, chronic otitis media, otitis media with effusion, and earwax plugs. ...
Objective:
To update the literature and provide a systematic review of image-based artificial intelligence (AI) applications in otolaryngology, highlight its advances, and propose future challenges.
Data sources:
Web of Science, Embase, PubMed, and Cochrane Library.
Review methods:
Studies written in English, published between January 2020 and December 2022. Two independent authors screened the search results, extracted data, and assessed studies.
Results:
Overall, 686 studies were identified. After screening titles and abstracts, 325 full-text studies were assessed for eligibility, and 78 studies were included in this systematic review. The studies originated from 16 countries. Among these countries, the top 3 were China (n = 29), Korea (n = 8), the United States, and Japan (n = 7 each). The most common area was otology (n = 35), followed by rhinology (n = 20), pharyngology (n = 18), and head and neck surgery (n = 5). Most applications of AI in otology, rhinology, pharyngology, and head and neck surgery mainly included chronic otitis media (n = 9), nasal polyps (n = 4), laryngeal cancer (n = 12), and head and neck squamous cell carcinoma (n = 3), respectively. The overall performance of AI in accuracy, the area under the curve, sensitivity, and specificity were 88.39 ± 9.78%, 91.91 ± 6.70%, 86.93 ± 11.59%, and 88.62 ± 14.03%, respectively.
Conclusion:
This state-of-the-art review aimed to highlight the increasing applications of image-based AI in otorhinolaryngology head and neck surgery. The following steps will entail multicentre collaboration to ensure data reliability, ongoing optimization of AI algorithms, and integration into real-world clinical practice. Future studies should consider 3-dimensional (3D)-based AI, such as 3D surgical AI.
