Accuracy of deep learning, a machine learning technology, using ultra-wide-field fundus ophthalmoscopy for detecting idiopathic macular holes

Nagasawa, Toshihiko; Tabuchi, Hitoshi; Masumoto, Hiroki; Enno, Hiroki; Niki, Masanori; Ohsugi, Hideharu; Mitamura, Yoshinori

doi:10.7717/peerj.5696

Cited by 32 publications

(29 citation statements)

References 20 publications

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“…However, to the best of our knowledge, no study has yet investigated the utility of a DNN model for image-based RP diagnosis. Most recently, several studies regarding the use of a DNN model to assess the UWPC image values for the diagnosis of retinal disorders have been reported by our team (Ohsugi et al, 2017; Nagasawa et al, 2018; Nagasato et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Accuracy of a deep convolutional neural network in detection of retinitis pigmentosa on ultrawide-field images

Masumoto¹,

Tabuchi²,

Nakakura³

et al. 2019

PeerJ

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Evaluating the discrimination ability of a deep convolution neural network for ultrawide-field pseudocolor imaging and ultrawide-field autofluorescence of retinitis pigmentosa. In total, the 373 ultrawide-field pseudocolor and ultrawide-field autofluorescence images (150, retinitis pigmentosa; 223, normal) obtained from the patients who visited the Department of Ophthalmology, Tsukazaki Hospital were used. Training with a convolutional neural network on these learning data objects was conducted. We examined the K-fold cross validation (K = 5). The mean area under the curve of the ultrawide-field pseudocolor group was 0.998 (95% confidence interval (CI) [0.9953–1.0]) and that of the ultrawide-field autofluorescence group was 1.0 (95% CI [0.9994–1.0]). The sensitivity and specificity of the ultrawide-field pseudocolor group were 99.3% (95% CI [96.3%–100.0%]) and 99.1% (95% CI [96.1%–99.7%]), and those of the ultrawide-field autofluorescence group were 100% (95% CI [97.6%–100%]) and 99.5% (95% CI [96.8%–99.9%]), respectively. Heatmaps were in accordance with the clinician’s observations. Using the proposed deep neural network model, retinitis pigmentosa can be distinguished from healthy eyes with high sensitivity and specificity on ultrawide-field pseudocolor and ultrawide-field autofluorescence images.

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Section: Introductionmentioning

confidence: 99%

Accuracy of a deep convolutional neural network in detection of retinitis pigmentosa on ultrawide-field images

Masumoto¹,

Tabuchi²,

Nakakura³

et al. 2019

PeerJ

Self Cite

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“…This study aimed to increase the accuracy of DL in diagnosing rare retinal diseases while maintaining the diagnostic performance for major diseases. Several previous studies have focused on building DL models for the diagnosis of rare retinal diseases, including macular hole [41], retinitis pigmentosa [42,43], and Stargardt disease [4]. However, these DL models were designed for binary classification using normal and pathological image data.…”

Section: Discussionmentioning

confidence: 99%

Feasibility study to improve deep learning in OCT diagnosis of rare retinal diseases with few-shot classification

Yoo

Choi

Kim

2021

Med Biol Eng Comput

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Deep learning (DL) has been successfully applied to the diagnosis of ophthalmic diseases. However, rare diseases are commonly neglected due to insufficient data. Here, we demonstrate that few-shot learning (FSL) using a generative adversarial network (GAN) can improve the applicability of DL in the optical coherence tomography (OCT) diagnosis of rare diseases. Four major classes with a large number of datasets and five rare disease classes with a few-shot dataset are included in this study. Before training the classifier, we constructed GAN models to generate pathological OCT images of each rare disease from normal OCT images. The Inception-v3 architecture was trained using an augmented training dataset, and the final model was validated using an independent test dataset. The synthetic images helped in the extraction of the characteristic features of each rare disease. The proposed DL model demonstrated a significant improvement in the accuracy of the OCT diagnosis of rare retinal diseases and outperformed the traditional DL models, Siamese network, and prototypical network. By increasing the accuracy of diagnosing rare retinal diseases through FSL, clinicians can avoid neglecting rare diseases with DL assistance, thereby reducing diagnosis delay and patient burden.

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“…3. The network architecture consisted of an input stage, a feature extraction stage with three convolutional layers, and an output classification stage (Nagasawa et al, 2018; Jang et al, 2018). The input stage received the Horn-Schunck algorithm applied color image which was converted to 170*150 pixels.…”

Section: Methodsmentioning

confidence: 99%

Identification of cecum time-location in a colonoscopy video by deep learning analysis of colonoscope movement

Cho

Kim

Hong

et al. 2019

PeerJ

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Background Cecal intubation time is an important component for quality colonoscopy. Cecum is the turning point that determines the insertion and withdrawal phase of the colonoscope. For this reason, obtaining information related with location of the cecum in the endoscopic procedure is very useful. Also, it is necessary to detect the direction of colonoscope’s movement and time-location of the cecum. Methods In order to analysis the direction of scope’s movement, the Horn–Schunck algorithm was used to compute the pixel’s motion change between consecutive frames. Horn–Schunk-algorithm applied images were trained and tested through convolutional neural network deep learning methods, and classified to the insertion, withdrawal and stop movements. Based on the scope’s movement, the graph was drawn with a value of +1 for insertion, −1 for withdrawal, and 0 for stop. We regarded the turning point as a cecum candidate point when the total graph area sum in a certain section recorded the lowest. Results A total of 328,927 frame images were obtained from 112 patients. The overall accuracy, drawn from 5-fold cross-validation, was 95.6%. When the value of “t” was 30 s, accuracy of cecum discovery was 96.7%. In order to increase visibility, the movement of the scope was added to summary report of colonoscopy video. Insertion, withdrawal, and stop movements were mapped to each color and expressed with various scale. As the scale increased, the distinction between the insertion phase and the withdrawal phase became clearer. Conclusion Information obtained in this study can be utilized as metadata for proficiency assessment. Since insertion and withdrawal are technically different movements, data of scope’s movement and phase can be quantified and utilized to express pattern unique to the colonoscopist and to assess proficiency. Also, we hope that the findings of this study can contribute to the informatics field of medical records so that medical charts can be transmitted graphically and effectively in the field of colonoscopy.

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Accuracy of deep learning, a machine learning technology, using ultra-wide-field fundus ophthalmoscopy for detecting idiopathic macular holes

Cited by 32 publications

References 20 publications

Accuracy of a deep convolutional neural network in detection of retinitis pigmentosa on ultrawide-field images

Accuracy of a deep convolutional neural network in detection of retinitis pigmentosa on ultrawide-field images

Feasibility study to improve deep learning in OCT diagnosis of rare retinal diseases with few-shot classification

Identification of cecum time-location in a colonoscopy video by deep learning analysis of colonoscope movement

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