Deep Learning Based Analysis of Ophthalmology: A Systematic Review

Abstract Objectives Deep learning (DL) has been increasingly employed for automated landmark detection, e.g., for cephalometric purposes. We performed a systematic review and meta-analysis to assess the accuracy and underlying evidence for DL for cephalometric landmark detection on 2-D and 3-D radiographs. Methods Diagnostic accuracy studies published in 2015-2020 in Medline/Embase/IEEE/arXiv and employing DL for cephalometric landmark detection were identified and extracted by two independent reviewers. Random-effects meta-analysis, subgroup, and meta-regression were performed, and study quality was assessed using QUADAS-2. The review was registered (PROSPERO no. 227498). Data From 321 identified records, 19 studies (published 2017–2020), all employing convolutional neural networks, mainly on 2-D lateral radiographs (n=15), using data from publicly available datasets (n=12) and testing the detection of a mean of 30 (SD: 25; range.: 7–93) landmarks, were included. The reference test was established by two experts (n=11), 1 expert (n=4), 3 experts (n=3), and a set of annotators (n=1). Risk of bias was high, and applicability concerns were detected for most studies, mainly regarding the data selection and reference test conduct. Landmark prediction error centered around a 2-mm error threshold (mean; 95% confidence interval: (–0.581; 95 CI: –1.264 to 0.102 mm)). The proportion of landmarks detected within this 2-mm threshold was 0.799 (0.770 to 0.824). Conclusions DL shows relatively high accuracy for detecting landmarks on cephalometric imagery. The overall body of evidence is consistent but suffers from high risk of bias. Demonstrating robustness and generalizability of DL for landmark detection is needed. Clinical significance Existing DL models show consistent and largely high accuracy for automated detection of cephalometric landmarks. The majority of studies so far focused on 2-D imagery; data on 3-D imagery are sparse, but promising. Future studies should focus on demonstrating generalizability, robustness, and clinical usefulness of DL for this objective.

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Deep learning for pulmonary embolism detection on computed tomography pulmonary angiogram: a systematic review and meta-analysis

Scientific Reports ◽

10.1038/s41598-021-95249-3 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Shelly Soffer ◽

Eyal Klang ◽

Orit Shimon ◽

Yiftach Barash ◽

Noa Cahan ◽

...

Keyword(s):

Systematic Review ◽

Pulmonary Embolism ◽

Deep Learning ◽

Sensitivity And Specificity ◽

Meta Analysis ◽

Risk Of Bias ◽

Future Research ◽

Learning Models ◽

Summary Receiver Operating Characteristic ◽

Pulmonary Angiogram

AbstractComputed tomographic pulmonary angiography (CTPA) is the gold standard for pulmonary embolism (PE) diagnosis. However, this diagnosis is susceptible to misdiagnosis. In this study, we aimed to perform a systematic review of current literature applying deep learning for the diagnosis of PE on CTPA. MEDLINE/PUBMED were searched for studies that reported on the accuracy of deep learning algorithms for PE on CTPA. The risk of bias was evaluated using the QUADAS-2 tool. Pooled sensitivity and specificity were calculated. Summary receiver operating characteristic curves were plotted. Seven studies met our inclusion criteria. A total of 36,847 CTPA studies were analyzed. All studies were retrospective. Five studies provided enough data to calculate summary estimates. The pooled sensitivity and specificity for PE detection were 0.88 (95% CI 0.803–0.927) and 0.86 (95% CI 0.756–0.924), respectively. Most studies had a high risk of bias. Our study suggests that deep learning models can detect PE on CTPA with satisfactory sensitivity and an acceptable number of false positive cases. Yet, these are only preliminary retrospective works, indicating the need for future research to determine the clinical impact of automated PE detection on patient care. Deep learning models are gradually being implemented in hospital systems, and it is important to understand the strengths and limitations of these algorithms.

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Deep Learning Methods for Heart Sounds Classification: A Systematic Review

Entropy ◽

10.3390/e23060667 ◽

2021 ◽

Vol 23 (6) ◽

pp. 667

Author(s):

Wei Chen ◽

Qiang Sun ◽

Xiaomin Chen ◽

Gangcai Xie ◽

Huiqun Wu ◽

...

Keyword(s):

Neural Network ◽

Systematic Review ◽

Deep Learning ◽

Heart Sounds ◽

Learning Approaches ◽

Automated Classification ◽

Learning Methods ◽

Artificial Intelligence Technology ◽

Medical Big Data ◽

Effective Models

The automated classification of heart sounds plays a significant role in the diagnosis of cardiovascular diseases (CVDs). With the recent introduction of medical big data and artificial intelligence technology, there has been an increased focus on the development of deep learning approaches for heart sound classification. However, despite significant achievements in this field, there are still limitations due to insufficient data, inefficient training, and the unavailability of effective models. With the aim of improving the accuracy of heart sounds classification, an in-depth systematic review and an analysis of existing deep learning methods were performed in the present study, with an emphasis on the convolutional neural network (CNN) and recurrent neural network (RNN) methods developed over the last five years. This paper also discusses the challenges and expected future trends in the application of deep learning to heart sounds classification with the objective of providing an essential reference for further study.

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Automatic image annotation based on deep learning models: a systematic review and future challenges

IEEE Access ◽

10.1109/access.2021.3068897 ◽

2021 ◽

pp. 1-1

Author(s):

Myasar Mundher Adnan ◽

Mohd Shafry Mohd Rahim ◽

Amjad Rehman ◽

Zahid Mehmood ◽

Tanzila Saba ◽

...

Keyword(s):

Systematic Review ◽

Deep Learning ◽

Image Annotation ◽

Automatic Image Annotation ◽

Learning Models ◽

Future Challenges

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The Application and Development of Deep Learning in Radiotherapy: A Systematic Review

Technology in Cancer Research & Treatment ◽

10.1177/15330338211016386 ◽

2021 ◽

Vol 20 ◽

pp. 153303382110163

Author(s):

Danju Huang ◽

Han Bai ◽

Li Wang ◽

Yu Hou ◽

Lan Li ◽

...

Keyword(s):

Artificial Intelligence ◽

Systematic Review ◽

Neural Networks ◽

Decision Making ◽

Deep Learning ◽

Radiation Oncology ◽

Clinical Work ◽

Radiation Oncologists ◽

Basic Concepts ◽

Further Development

With the massive use of computers, the growth and explosion of data has greatly promoted the development of artificial intelligence (AI). The rise of deep learning (DL) algorithms, such as convolutional neural networks (CNN), has provided radiation oncologists with many promising tools that can simplify the complex radiotherapy process in the clinical work of radiation oncology, improve the accuracy and objectivity of diagnosis, and reduce the workload, thus enabling clinicians to spend more time on advanced decision-making tasks. As the development of DL gets closer to clinical practice, radiation oncologists will need to be more familiar with its principles to properly evaluate and use this powerful tool. In this paper, we explain the development and basic concepts of AI and discuss its application in radiation oncology based on different task categories of DL algorithms. This work clarifies the possibility of further development of DL in radiation oncology.

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Plant Disease Identification Using Deep Learning: A Systematic Review

2021 2nd International Conference on Intelligent Engineering and Management (ICIEM) ◽

10.1109/iciem51511.2021.9445277 ◽

2021 ◽

Author(s):

Anita Sharma ◽

Kamlesh Lakhwani ◽

Harmeet Singh Janeja

Keyword(s):

Systematic Review ◽

Deep Learning ◽

Plant Disease ◽

Disease Identification

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Convolutional Extreme Learning Machines: A Systematic Review

10.20944/preprints202104.0753.v1 ◽

2021 ◽

Author(s):

Iago Richard Rodrigues ◽

Sebastião Rogério ◽

Judith Kelner ◽

Djamel Sadok ◽

Patricia Takako Endo

Keyword(s):

Systematic Review ◽

Deep Learning ◽

Extreme Learning Machine ◽

Research Area ◽

New Paradigm ◽

Learning Machines ◽

Starting Point ◽

Benchmark Datasets ◽

Good Starting Point ◽

Learning Machine

Many works have recently identified the need to combine deep learning with extreme learning to strike a performance balance with accuracy especially in the domain of multimedia applications. Considering this new paradigm, namely convolutional extreme learning machine (CELM), we present a systematic review that investigates alternative deep learning architectures that use extreme learning machine (ELM) for a faster training to solve problems based on image analysis. We detail each of the architectures found in the literature, application scenarios, benchmark datasets, main results, advantages, and present the open challenges for CELM. We follow a well structured methodology and establish relevant research questions that guide our findings. We hope that the observation and classification of such works can leverage the CELM research area providing a good starting point to cope with some of the current problems in the image-based computer vision analysis.

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