Diagnosis, Treatment, and Management of Otitis Media with Artificial Intelligence
Abstract
:1. Introduction
2. Artificial Intelligence & Machine Learning
2.1. What
2.2. How
2.3. Why
3. Materials and Methods
3.1. Literature Search
3.2. Selection Criteria
3.3. Data Extraction
4. Diagnosis
4.1. Computer Vision
4.1.1. Otoscopy
4.1.2. Radiology & Pathology
4.1.3. Tympanometry
4.2. Natural Language Processing
5. Treatment
5.1. Internal Medicine
5.2. Surgery
6. Risk Prediction & Postoperative Care
7. Challenges and Future Considerations
7.1. Data & Algorithm
7.2. Application
7.3. Privacy & Regulation
8. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
Abbreviations | Extensions |
ENT | Ear-nose-throat |
OM | Otitis media |
OME | Otitis media effusion |
AOM | Acute otitis media |
COM | Chronic otitis media |
CSOM | Chronic suppurative otitis media |
PCD | Primary ciliated dyskinesia |
TM | Tympanic membrane |
ABG | Air-bone gap |
FNDs | Fluorescent nanodiamonds |
VPO | Video pneumatic otoscope |
OCT | Optical coherence tomography |
WBT | Wideband tympanometry |
AI | Artificial intelligence |
CNN | Convolutional neural networks |
ML | Machine learning |
DL | Deep learning |
CV | Computer vision |
NLP | Natural language processing |
XAI | Explainable artificial intelligence |
Grad-CAM | Gradient-weighted Class Activation Mapping |
BOW | Bag-of-words model |
NCA | Neighborhood component analysis |
FNN | Feedforward artificial neural networks |
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ML Algorithms | Definition |
---|---|
Supervised learning | The model is trained based on the labeled data to make the prediction. It is mainly used for classification (continuous data) and regression problems (discrete data). |
Ensemble learning | Generate multiple models and then combine these models according to a certain method. It is mainly applied to improve model performance or reduce the possibility of improper model selection. |
Bagging-Random forest (RF) | As a type of ensembled learning, it is based on decision trees. Each of decision trees can produce an independent and de-correlated output. And then the final result is determined by the rule of “majority-vote”. Each decision tree has the equal weight. |
Boosting-Gradient boosting machine (GBM) | The basic unit is the decision tree. The performance of the first decision tree is only due to random guessing. The decision tree model established later are based on the prior one by adjusting parameters to gradually improve the accuracy of subsequent models. And then the final result is determined by considering comprehensively the predicted values of all decision trees. Base learner (decision tree) with better performance has higher weight. |
Support vector machine (SVM) | In a given set of training samples, with functional distance as constraint condition and geometric distance as objective function, the maximum-margin hyperplane with the best generalization ability and the strongest robust is determined, so as to realize the binary classification of data. |
Unsupervised learning | The training data is not labeled and the goal is to find patterns in the data. It is mainly used to solve clustering and dimension reduction problems. |
Clustering | The process of sorting data into different classes or clusters based on mathematical relevance. |
Dimensionality reduction | It means converting high-dimensional data into a lower representation with fewer features and is often used for data visualization or data preprocessing. |
Reinforcement learning | Through interaction with its environment and trial and error, the training model learns the environment-to-action reflex that maximize cumulative returns. |
Transfer learning (TL) | Transfer the learned knowledge from a domain to another and save a deal of time and computing resources for the training. |
Deep learning (DL) | Mimicking the hierarchical network of neurons in the brain and using multiple layers of data processing, the model can automatically detect the required features and predict the result. But it requires larger quantities of data and advanced computational capacity. |
Feedforward neural networks (FNN) | Arranged in layers, each neuron receives only the output of the previous layer and sends it to the next layer. There is no feedback between adjacent layers. |
Convolutional neural networks (CNN) | It is often used in imaging analysis. Through the convolution layer, activation function and pooling layer, the input image is approximate processed and redundant features are removed to reduce the computational complexity and help prevent overfitting. And then the significant features are combined through the fully connected layer to output prediction or classification. |
Natural language processing (NLP) | Algorithms are established to organize and interpret human language. The aim is to realize the interactive communication between humans and machines. |
Computer vision (CV) | Algorithms are established to enable the computer to perceive, observe and understand the environment through images and vision, and finally have the ability to adapt to the environment. |
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Share and Cite
Ding, X.; Huang, Y.; Tian, X.; Zhao, Y.; Feng, G.; Gao, Z. Diagnosis, Treatment, and Management of Otitis Media with Artificial Intelligence. Diagnostics 2023, 13, 2309. https://doi.org/10.3390/diagnostics13132309
Ding X, Huang Y, Tian X, Zhao Y, Feng G, Gao Z. Diagnosis, Treatment, and Management of Otitis Media with Artificial Intelligence. Diagnostics. 2023; 13(13):2309. https://doi.org/10.3390/diagnostics13132309
Chicago/Turabian StyleDing, Xin, Yu Huang, Xu Tian, Yang Zhao, Guodong Feng, and Zhiqiang Gao. 2023. "Diagnosis, Treatment, and Management of Otitis Media with Artificial Intelligence" Diagnostics 13, no. 13: 2309. https://doi.org/10.3390/diagnostics13132309
APA StyleDing, X., Huang, Y., Tian, X., Zhao, Y., Feng, G., & Gao, Z. (2023). Diagnosis, Treatment, and Management of Otitis Media with Artificial Intelligence. Diagnostics, 13(13), 2309. https://doi.org/10.3390/diagnostics13132309