Journal: Journal of Imaging

Article Title: Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey

doi: 10.3390/jimaging7090165

Figure Lengend Snippet: Classification-based studies in DR detection using fundus imaging.

Article Snippet: Samanta, 2020 [ ] , EyePACS , Grade DR based on ICDR scale , Yes , DCNN: DenseNet121 based , 84.1% , NA , NA , NA.

Techniques: Imaging, Modification, Extraction

Journal: Journal of Ophthalmology

Article Title: Applications of Artificial Intelligence in Ophthalmology: General Overview

doi: 10.1155/2018/5278196

Figure Lengend Snippet: Studies on eye diseases using DL techniques.

Article Snippet: Burlina et al. [ ] (Retina Division, Wilmer Eye Institute, Johns Hopkins University School of Medicine) , AMD Grading , Public: AREDS 5664 fundus images , DCNN , Accuracy 79.4% (4-class) 81.5% (3-class) 93.4% (2-class) , Demonstrates comparable performance between computer and physician grading.

Techniques: Injection, Medications, Biomarker Discovery

Journal: Eye and Vision

Article Title: Application of machine learning in ophthalmic imaging modalities

doi: 10.1186/s40662-020-00183-6

Figure Lengend Snippet: Summary of DL methods using FP and OCT to detect eye disease

Article Snippet: Gargeya R et al. [ ] , 2017 , FP , Automated identification of DR , Public: EyePACS, 75,137 FPs MESSIDOR 2, 1748 E-Ophtha, 463 FPs , DCNN , Sensitivity: 94% Specificity: 98% AUC: 0.97.

Techniques: Imaging, Modification, Biomarker Discovery, Medications

Journal: European Journal of Gastroenterology & Hepatology

Article Title: Computer-aided detection of colorectal polyps using a newly generated deep convolutional neural network: from development to first clinical experience

doi: 10.1097/MEG.0000000000002209

Figure Lengend Snippet: Development and diagnostic output of the system. (a) The deep convolutional neural network (DCNN) processes video data as a sequence of single video frames and generates predictions based on the visual evidence of a single video frame. The predictions from individual frames are then fused to provide a more stable detection. (b) Different examples of polyp detection with the DCNN during routine colonoscopy. The computer-aided detection (CAD) system generates the diagnostic output on a second screen on which polyps are highlighted by a bounding box. Note that the DCNN is able to detect multiple polyps in a single frame simultaneously (upper right picture).

Article Snippet: In the current study, we evaluated a novel deep convolutional neural network (DCNN) for automated detection of colorectal polyps that has been developed by a manufacturer of the healthcare industry (Hoya Corporation, Pentax Medical Division, Digital Endoscopy, Friedberg, Germany) in close collaboration with clinical and scientific partners and assessed the performance of the DCNN ex vivo as well as in a first in-human pilot trial.

Techniques: Diagnostic Assay, Sequencing

Journal: European Journal of Gastroenterology & Hepatology

Article Title: Computer-aided detection of colorectal polyps using a newly generated deep convolutional neural network: from development to first clinical experience

doi: 10.1097/MEG.0000000000002209

Figure Lengend Snippet: Patient characteristics and withdrawal times

Article Snippet: In the current study, we evaluated a novel deep convolutional neural network (DCNN) for automated detection of colorectal polyps that has been developed by a manufacturer of the healthcare industry (Hoya Corporation, Pentax Medical Division, Digital Endoscopy, Friedberg, Germany) in close collaboration with clinical and scientific partners and assessed the performance of the DCNN ex vivo as well as in a first in-human pilot trial.

Techniques:

Journal: European Journal of Gastroenterology & Hepatology

Article Title: Computer-aided detection of colorectal polyps using a newly generated deep convolutional neural network: from development to first clinical experience

doi: 10.1097/MEG.0000000000002209

Figure Lengend Snippet: Total number of polyps and adenomas and polyp detection rate and adenoma detection rate after first (without deep convolutional neural network) and second inspection (with deep convolutional neural network)

Article Snippet: In the current study, we evaluated a novel deep convolutional neural network (DCNN) for automated detection of colorectal polyps that has been developed by a manufacturer of the healthcare industry (Hoya Corporation, Pentax Medical Division, Digital Endoscopy, Friedberg, Germany) in close collaboration with clinical and scientific partners and assessed the performance of the DCNN ex vivo as well as in a first in-human pilot trial.

Techniques:

Journal: European Journal of Gastroenterology & Hepatology

Article Title: Computer-aided detection of colorectal polyps using a newly generated deep convolutional neural network: from development to first clinical experience

doi: 10.1097/MEG.0000000000002209

Figure Lengend Snippet: Characteristics of the polyps detected during first inspection without deep convolutional neural network and those additionally detected during second inspection with deep convolutional neural network

Article Snippet: In the current study, we evaluated a novel deep convolutional neural network (DCNN) for automated detection of colorectal polyps that has been developed by a manufacturer of the healthcare industry (Hoya Corporation, Pentax Medical Division, Digital Endoscopy, Friedberg, Germany) in close collaboration with clinical and scientific partners and assessed the performance of the DCNN ex vivo as well as in a first in-human pilot trial.

Techniques:

Journal: Frontiers in Neuroscience

Article Title: A review of epilepsy detection and prediction methods based on EEG signal processing and deep learning

doi: 10.3389/fnins.2024.1468967

Figure Lengend Snippet: Denoising methods for epileptic EEG signals.

Article Snippet: , EPILEPSIAE , DCNN , Evaluation indicators are better than Infomax ICA-MARA.

Techniques: Residue, Selection

Journal: Journal of Ophthalmology

Article Title: Applications of Artificial Intelligence in Ophthalmology: General Overview

doi: 10.1155/2018/5278196

Figure Lengend Snippet: Concise introduction of CNN algorithms used in AI diagnosis.

Article Snippet: Burlina et al. [ ] (Retina Division, Wilmer Eye Institute, Johns Hopkins University School of Medicine) , AMD detection , Public: AREDS 130000 fundus images , DCNN (AlexNet) , AUC: 0.94∼0.96 Accuracy: 88.4%∼91.6% , Applying a DL-based automated assessment of AMD from fundus images can produce results that are similar to human performance levels.

Techniques: Biomarker Discovery

Journal: Journal of Ophthalmology

Article Title: Applications of Artificial Intelligence in Ophthalmology: General Overview

doi: 10.1155/2018/5278196

Figure Lengend Snippet: Studies on eye diseases using DL techniques.

Article Snippet: Burlina et al. [ ] (Retina Division, Wilmer Eye Institute, Johns Hopkins University School of Medicine) , AMD detection , Public: AREDS 130000 fundus images , DCNN (AlexNet) , AUC: 0.94∼0.96 Accuracy: 88.4%∼91.6% , Applying a DL-based automated assessment of AMD from fundus images can produce results that are similar to human performance levels.

Techniques: Injection, Medications, Biomarker Discovery