A Web-Based Deep Learning Model for Automated Diagnosis of Otoscopic Images

Tsutsumi, Kotaro; Goshtasbi, Khodayar; Risbud, Adwight; Khosravi, Pooya; Pang, Ji‐Jie; Lin, Harrison W.; Djalilian, Hamid R.; Abouzari, Mehdi

doi:10.1097/mao.0000000000003210

Cited by 18 publications

(14 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…AI algorithms achieved a pooled accuracy of 90.7% (95%CI: 90.1-91.3%) to difference between normal or abnormal otoscopy images with substantial heterogeneity between studies (n = 14 studies, I 2 = 96.9%, p = .001, Figure 3). Four studies [14][15][16][17] used the Özel Van Akdamar Hospital otoscopic image database to train binary algorithms using various classification techniques. Simon et al 14 demonstrated that pretrained convolutional neural networks (CNNs) and support vector machines (SVMs) could achieve greater classification accuracy than k-nearest neighbours (k-NNs), artificial neural networks (ANNs), decision trees (DTs) or the Naïve Bayes technique.…”

Section: Normal Versus Abnormalmentioning

confidence: 99%

“…Four studies [14][15][16][17] used the Özel Van Akdamar Hospital otoscopic image database to train binary algorithms using various classification techniques. Simon et al 14 demonstrated that pretrained convolutional neural networks (CNNs) and support vector machines (SVMs) could achieve greater classification accuracy than k-nearest neighbours (k-NNs), artificial neural networks (ANNs), decision trees (DTs) or the Naïve Bayes technique.…”

Section: Normal Versus Abnormalmentioning

confidence: 99%

See 1 more Smart Citation

Artificial intelligence to classify ear disease from otoscopy: A systematic review and meta‐analysis

Habib

Kajbafzadeh

Hasan

et al. 2022

Clinical Otolaryngology

View full text Add to dashboard Cite

Objectives:To summarise the accuracy of artificial intelligence (AI) computer vision algorithms to classify ear disease from otoscopy.Design:Systematic review and meta-analysis.Methods:Using the PRISMA guidelines, nine online databases were searched for articles that used AI computer vision algorithms developed from various methods (convolutional neural networks, artificial neural networks, support vector machines, decision trees and k-nearest neighbours) to classify otoscopic images. Diagnostic classes of interest: normal tympanic membrane, acute otitis media (AOM), otitis media with effusion (OME), chronic otitis media (COM) with or without perforation, cholesteatoma and canal obstruction.Mainoutcomemeasures:Accuracy to correctly classify otoscopic images compared to otolaryngologists (ground truth). The Quality Assessment of Diagnostic Accuracy Studies Version 2 tool was used to assess the quality of methodology and risk of bias.Results: Thirty-nine articles were included. Algorithms achieved 90.7% (95%CI: 90.1-91.3%) accuracy to difference between normal or abnormal otoscopy images in 14 studies. The most common multiclassification algorithm (3 or more diagnostic classes) achieved 97.6% (95%CI: 97.3-97.9%) accuracy to differentiate between normal, AOM and OME in three studies. AI algorithms outperformed human assessors to classify otoscopy images achieving 93.4% (95%CI: 90.5-96.4%) versus 73.2% (95%CI: 67.9-78.5%) accuracy in three studies. Convolutional neural networks achieved the highest accuracy compared to other classification methods. Conclusion:AI can classify ear disease from otoscopy. A concerted effort is requiredto establish a comprehensive and reliable otoscopy database for algorithm training.An AI-supported otoscopy system may assist health care workers, trainees and primary care practitioners with less otology experience identify ear disease.

show abstract

Section: Normal Versus Abnormalmentioning

confidence: 99%

Section: Normal Versus Abnormalmentioning

confidence: 99%

Artificial intelligence to classify ear disease from otoscopy: A systematic review and meta‐analysis

Habib

Kajbafzadeh

Hasan

et al. 2022

Clinical Otolaryngology

View full text Add to dashboard Cite

show abstract

“…In [121], an ear infection classifier 62 was implemented and trained with published otoscopic images, from which transfer learning on MobileNetV2 was found to outperform Inception-V3, ResNet-50, and InceptionResnet-V2. In [57], the viability of Google's Teachable Machine was assessed against the diagnosis of tooth-marked tongue, a condition in which tooth traces develop on the tongue.…”

Section: Healthcare Diagnosismentioning

confidence: 99%

Front-end deep learning web apps development and deployment: a review

Goh

Abas

2022

Appl Intell

View full text Add to dashboard Cite

Machine learning and deep learning models are commonly developed using programming languages such as Python, C++, or R and deployed as web apps delivered from a back-end server or as mobile apps installed from an app store. However, recently front-end technologies and JavaScript libraries, such as TensorFlow.js, have been introduced to make machine learning more accessible to researchers and end-users. Using JavaScript, TensorFlow.js can define, train, and run new or existing, pre-trained machine learning models entirely in the browser from the client-side, which improves the user experience through interaction while preserving privacy. Deep learning models deployed on front-end browsers must be small, have fast inference, and ideally be interactive in real-time. Therefore, the emphasis on development and deployment is different. This paper aims to review the development and deployment of these deep-learning web apps to raise awareness of the recent advancements and encourage more researchers to take advantage of this technology for their own work. First, the rationale behind the deployment stack (front-end, JavaScript, and TensorFlow.js) is discussed. Then, the development approach for obtaining deep learning models that are optimized and suitable for front-end deployment is then described. The article also provides current web applications divided into seven categories to show deep learning potential on the front end. These include web apps for deep learning playground, pose detection and gesture tracking, music and art creation, expression detection and facial recognition, video segmentation, image and signal analysis, healthcare diagnosis, recognition, and identification.

show abstract

“…There is a growing increase in the number of web-based implementations of deep learning frameworks that provide convenient public access and ease of implementation [1][2][3][4][5][6]. Notably, many web servers have been developed for sequence design tasks, like analysis of RNA, DNA, or proteins.…”

Section: Introductionmentioning

confidence: 99%

“…[3] There is a growing increase in the number of web-based implementations of deep learning frameworks that provide convenient public access and ease of use. [4][5][6][7][8][9] Notably, many web servers have been developed for sequence design tasks, like analysis of RNA, DNA, or proteins. For example, survival analysis based on mRNA data (GENT2, [10] PROGgeneV2, [11] SurvExpress, [12] MEXPRESS, [13] etc.…”

Section: Introductionmentioning

confidence: 99%

Serverless Prediction of Peptide Properties with Recurrent Neural Networks

Ansari

White

2022

Preprint

View full text Add to dashboard Cite

We present three deep learning sequence prediction models for hemolysis, solubility, and resistance to non-specific interactions of peptides that achieve comparable results to the state-of-the art models. These predictive models share a common architecture of bidirectional recurrent neural networks (LSTM). These models are implemented in JavaScript so that they can be run on a static website without use of a dedicated server. This removes the cost, and long-term management of a server, while still enabling open and free access to the models. This "serverless" prediction model is a demonstration of edge computing bioinformatics and removes the dependence on cloud providers or self-hosting of resource-rich academic institutions. This is feasible because of the continued track of Moore's law and ubiquitous hardware acceleration of deep learning computations on new phones and desktops.

show abstract

A Web-Based Deep Learning Model for Automated Diagnosis of Otoscopic Images

Cited by 18 publications

References 26 publications

Artificial intelligence to classify ear disease from otoscopy: A systematic review and meta‐analysis

Artificial intelligence to classify ear disease from otoscopy: A systematic review and meta‐analysis

Front-end deep learning web apps development and deployment: a review

Serverless Prediction of Peptide Properties with Recurrent Neural Networks

Contact Info

Product

Resources

About