[Deep learning and neuronal networks in ophthalmology : Applications in the field of optical coherence tomography]

Discriminating Healthy Optic Discs and Visible Optic Disc Drusen on Fundus Autofluorescence and Color Fundus Photography Using Deep Learning-A Pilot Study

The aim of this study was to use deep learning based on a deep convolutional neural network (DCNN) for automated image classification of healthy optic discs (OD) and visible optic disc drusen (ODD) on fundus autofluorescence (FAF) and color fundus photography (CFP). In this study, a total of 400 FAF and CFP images of patients with ODD and healthy controls were used. A pre-trained multi-layer Deep Convolutional Neural Network (DCNN) was trained and validated independently on FAF and CFP images. Training and validation accuracy and cross-entropy were recorded. Both generated DCNN classifiers were tested with 40 FAF and CFP images (20 ODD and 20 controls). After the repetition of 1000 training cycles, the training accuracy was 100%, the validation accuracy was 92% (CFP) and 96% (FAF), respectively. The cross-entropy was 0.04 (CFP) and 0.15 (FAF). The sensitivity, specificity, and accuracy of the DCNN for classification of FAF images was 100%. For the DCNN used to identify ODD on color fundus photographs, sensitivity was 85%, specificity 100%, and accuracy 92.5%. Differentiation between healthy controls and ODD on CFP and FAF images was possible with high specificity and sensitivity using a deep learning approach.

Magnetic Resonance Imaging Images under Deep Learning in the Identification of Tuberculosis and Pneumonia

This work aimed to explore the application value of deep learning-based magnetic resonance imaging (MRI) images in the identification of tuberculosis and pneumonia, in order to provide a certain reference basis for clinical identification. In this study, 30 pulmonary tuberculosis patients and 27 pneumonia patients who were hospitalized were selected as the research objects, and they were divided into a pulmonary tuberculosis group and a pneumonia group. MRI examination based on noise reduction algorithms was used to observe and compare the signal-to-noise ratio (SNR) and carrier-to-noise ratio (CNR) of the images. In addition, the apparent diffusion coefficient (ADC) value for the diagnosis efficiency of lung parenchymal lesions was analyzed, and the best b value was selected. The results showed that the MRI image after denoising by the deep convolutional neural network (DCNN) algorithm was clearer, the edges of the lung tissue were regular, the inflammation signal was higher, and the SNR and CNR were better than before, which were 119.79 versus 83.43 and 12.59 versus 7.21, respectively. The accuracy of MRI based on a deep learning algorithm in the diagnosis of pulmonary tuberculosis and pneumonia was significantly improved (96.67% vs. 70%, 100% vs. 62.96%) (P < 0.05). With the increase in b value, the CNR and SNR of MRI images all showed a downward trend (P < 0.05). Therefore, it was found that the shadow of tuberculosis lesions under a specific sequence was higher than that of pneumonia in the process of identifying tuberculosis and pneumonia, which reflected the importance of deep learning MRI images in the differential diagnosis of tuberculosis and pneumonia, thereby providing reference basis for clinical follow-up diagnosis and treatment.

[Employee survey after introduction of the FIDUS electronic patient file at the Saarland University Eye Hospital]

Ziel

Ziel der Studie war es, die Zufriedenheit der Mitarbeiterinnen und Mitarbeiter der Universitätsaugenklinik des Saarlandes mit der elektronischen Patientenakte FIDUS zwischen Dezember 2016 und September 2020 zu vergleichen, nachdem sie im Januar 2016 eingeführt worden war.

Methoden

Um diese Studie durchzuführen, hat das FIDUS-Team der Universitätsaugenklinik des Saarlandes einen Fragebogen erstellt. Dieser wurde im Dezember 2016 und im September 2020 an alle Beteiligten verteilt: ärztliches Personal, Pflege- und Verwaltungspersonal. Der Bogen enthielt 19 Fragen, die auf einer Skala von 0 für („stimme nicht zu“) bis 10 („stimme vollständig zu“) zu beantworten waren.

Ergebnisse

Die Anzahl der Teilnehmenden hatte von 60 (44 %) auf 89 (64 %) zwischen 2016 und 2020 deutlich zugenommen. Davon waren 23 (25,8 %) dem ärztlichen und 27 (30,3 %) dem pflegerischen Personal zuzuordnen, 12 (13,4 %) waren Verwaltungsbereich, 16 (17,9 %) in einem sonstigen Arbeitsverhältnis, und 11 (12,3 %) machten keine Angabe. Im Jahr 2020 haben 75,6 % der Befragten auf der Skala mit „10“ zugestimmt, dass die Einführung der elektronischen Patientenakte der richtige Schritt war, im Vergleich zu 36,7 % im Jahr 2016 (p < 0,001). Die Höchstbewertung, dass „die Akteneinträge mit dem Computer schneller zu schreiben sind als handschriftlich“, lag bei 38 % im Jahr 2020 vs. 25 % im Jahr 2016 (p < 0,001). Im Jahr 2020 befürworteten 61,6 % der Befragten vollständig die Einführung der papierlosen Patientenaufklärung als nächsten Schritt.

Schlussfolgerung

Die Zufriedenheit mit der elektronischen Patientenakte FIDUS hat sich im Jahr 2020 im Vergleich zu 2016 signifikant verbessert. Das gilt insbesondere für die bessere Übersichtlichkeit der Akte und die schnelleren Arbeitsabläufe. Allerdings scheint noch Optimierungsbedarf im Detail zu bestehen.

[Diagnostics of diseases of the optic nerve head in times of artificial intelligence and big data]

Hintergrund

Der Einsatz von künstlicher Intelligenz (KI) ist unter anderem in der automatischen Bildsegmentierung, -analyse und Klassifikation interessant und bereits für verschiedene Bereiche der Augenheilkunde beschrieben.

Fragestellung

Diese Arbeit soll einen Überblick über aktuelle Ansätze und Fortschritte bei der Anwendung von Big Data und KI bei verschiedenen Erkrankungen des Sehnervenkopfes geben.

Material und Methode

Es wurde eine PubMed-Recherche durchgeführt. Gesucht wurde nach Studien, die klinische Fragestellungen mithilfe von Big-Data-Ansätzen beantworteten oder klassische Methoden des maschinellen Lernens bei der Analyse von multimodaler Bildgebung des Sehnervenkopfes verwendeten.

Ergebnisse

Big Data kann bei Volkskrankheiten wie dem Glaukom helfen, klinische Fragestellungen zu beantworten. KI findet sowohl bei der Segmentierung von multimodaler Bildgebung des Sehnervenkopfes als auch bei der Klassifikation von Erkrankungen wie dem Glaukom oder der Stauungspapille auf diesen Bilddaten Anwendung.

Schlussfolgerung

Mithilfe von Big Data und KI können Zusammenhänge besser erkannt und die Diagnostik und Verlaufsbeurteilung von Erkrankungen des Sehnervenkopfes erleichtert oder automatisiert werden. Eine Voraussetzung für die klinische Anwendung ist in Europa die CE-Kennzeichnung als ein Medizinprodukt und in den USA die Zulassung durch die Food and Drug Administration.

[Artificial intelligence in management of macular edema : Opportunities and challenges]

Macular edema occurs in a wide variety of ophthalmological diseases. The diagnostics and treatment are an important part of modern ophthalmology. Due to the continuous development, artificial intelligence (AI) offers many opportunities to improve the management of macular edema. This article provides the readership with an overview of this interesting topic.

Extraction of Retinal Layers Through Convolution Neural Network (CNN) in an OCT Image for Glaucoma Diagnosis

Glaucoma is a progressive and deteriorating optic neuropathy that leads to visual field defects. The damage occurs as glaucoma is irreversible, so early and timely diagnosis is of significant importance. The proposed system employs the convolution neural network (CNN) for automatic segmentation of the retinal layers. The inner limiting membrane (ILM) and retinal pigmented epithelium (RPE) are used to calculate cup-to-disc ratio (CDR) for glaucoma diagnosis. The proposed system uses structure tensors to extract candidate layer pixels, and a patch across each candidate layer pixel is extracted, which is classified using CNN. The proposed framework is based upon VGG-16 architecture for feature extraction and classification of retinal layer pixels. The output feature map is merged into SoftMax layer for classification and produces probability map for central pixel of each patch and decides whether it is ILM, RPE, or background pixels. Graph search theory refines the extracted layers by interpolating the missing points, and these extracted ILM and RPE are finally used to compute CDR value and diagnose glaucoma. The proposed system is validated using a local dataset of optical coherence tomography images from 196 patients, including normal and glaucoma subjects. The dataset contains manually annotated ILM and RPE layers; manually extracted patches for ILM, RPE, and background pixels; CDR values; and eventually final finding related to glaucoma. The proposed system is able to extract ILM and RPE with a small absolute mean error of 6.03 and 5.56, respectively, and it finds CDR value within average range of ± 0.09 as compared with glaucoma expert. The proposed system achieves average sensitivity, specificity, and accuracies of 94.6, 94.07, and 94.68, respectively.

[FIDUSweb version 2.0 : An electronic platform to establish a cooperative university eye network]

BACKGROUND

Due to the increasing digitalization in ophthalmology, increasingly more documentation and diagnostic data are becoming electronically available. In order to improve patient care beyond sectoral boundaries, electronic cooperation portals were introduced in Münster and Homburg, which serve the networking with ophthalmologists in private practice.

METHOD

This article reports on the implementation of an electronic cooperation portal at the University Eye Hospitals in Münster and Homburg in 2015 and 2016.

RESULTS

The FIDUSweb represents a data protection-compliant cooperation portal that can be easily set up. By implementing it as a web portal all terminal equipment and also electronic patient files can be networked. Particularly important functions are the transmission of medical reports in real time, appointment booking functions, the transmission of imaging data and shared treatment documentation.

CONCLUSION

Cross-sectoral care can greatly benefit from the use of the FIDUSweb, especially in digital and multimodal ophthalmology.

Automated OCT angiography image quality assessment using a deep learning algorithm

PURPOSE

To expedite and to standardize the process of image quality assessment in optical coherence tomography angiography (OCTA) using a specialized deep learning algorithm (DLA).

METHODS

Two hundred randomly chosen en-face macular OCTA images of the central 3 × 3 mm² superficial vascular plexus were evaluated retrospectively by an OCTA experienced reader. Images were defined either as sufficient (group 1, n = 100) or insufficient image quality (group 2, n = 100) based on Motion Artifact Score (MAS) and Segmentation Accuracy Score (SAS). Subsequently, a pre-trained multi-layer deep convolutional neural network (DCNN) was trained and validated with 160 of these en-face OCTA scans (group 1: 80; group 2: 80). Training accuracy, validation accuracy, and cross-entropy were computed. The DLA was tested in detecting 40 untrained OCTA images (group 1: 20; group 2: 20). An insufficient image quality probability score (IPS) and a sufficient image quality probability score (SPS) were calculated.

RESULTS

Training accuracy was 97%, validation accuracy 100%, and cross entropy 0.12. A total of 90% (18/20) of the OCTA images with insufficient image quality and 90% (18/20) with sufficient image quality were correctly classified by the DLA. Mean IPS was 0.88 ± 0.21, and mean SPS was 0.84 ± 0.19. Discrimination between both groups was highly significant (p < 0.001). Sensitivity of the DLA was 90.0%, specificity 90.0%, and accuracy 90.0%. Coefficients of variation were 0.96 ± 1.9% (insufficient quality) and 1.14 ± 1.6% (sufficient quality).

CONCLUSIONS

Deep learning (DL) appears to be a potential approach to automatically distinguish between sufficient and insufficient OCTA image quality. DL may contribute to establish image quality standards in this recent imaging modality.