Active and inactive microaneurysms identified and characterized by structural and angiographic optical coherence tomography

Purpose: To characterize flow status within microaneurysms (MAs) and quantitatively investigate their relations with regional macular edema in diabetic retinopathy (DR). Design: Retrospective, cross-sectional study. Participants: A total of 99 participants, including 23 with mild nonproliferative DR (NPDR), 25 with moderate NPDR, 34 with severe NPDR, 17 with proliferative DR. Methods: In this study, 3x3-mm optical coherence tomography (OCT) and OCT angiography (OCTA) scans with a 400x400 sampling density from one eye of each participant were obtained using a commercial OCT system. Trained graders manually identified MAs and their location relative to the anatomic layers from cross-sectional OCT. Microaneurysms were first classified as active if the flow signal was present in the OCTA channel. Then active MAs were further classified into fully active and partially active MAs based on the flow perfusion status of MA on en face OCTA. The presence of retinal fluid near MAs was compared between active and inactive types. We also compared OCT-based MA detection to fundus photography (FP) and fluorescein angiography (FA)-based detection. Results: We identified 308 MAs (166 fully active, 88 partially active, 54 inactive) in 42 eyes using OCT and OCTA. Nearly half of the MAs identified straddle the inner nuclear layer and outer plexiform layer. Compared to partially active and inactive MAs, fully active MAs were more likely to be associated with local retinal fluid. The associated fluid volumes were larger with fully active MAs than with partially active and inactive MAs. OCT/OCTA detected all MAs found on FP. While not all MAs seen with FA were identified with OCT, some MAs seen with OCT were not visible with FA or FP. Conclusions: Co-registered OCT and OCTA can characterize MA activities, which could be a new means to study diabetic macular edema pathophysiology

Interpretable Diabetic Retinopathy Diagnosis based on Biomarker Activation Map

Deep learning classifiers provide the most accurate means of automatically diagnosing diabetic retinopathy (DR) based on optical coherence tomography (OCT) and its angiography (OCTA). The power of these models is attributable in part to the inclusion of hidden layers that provide the complexity required to achieve a desired task. However, hidden layers also render algorithm outputs difficult to interpret. Here we introduce a novel biomarker activation map (BAM) framework based on generative adversarial learning that allows clinicians to verify and understand classifiers decision-making. A data set including 456 macular scans were graded as non-referable or referable DR based on current clinical standards. A DR classifier that was used to evaluate our BAM was first trained based on this data set. The BAM generation framework was designed by combing two U-shaped generators to provide meaningful interpretability to this classifier. The main generator was trained to take referable scans as input and produce an output that would be classified by the classifier as non-referable. The BAM is then constructed as the difference image between the output and input of the main generator. To ensure that the BAM only highlights classifier-utilized biomarkers an assistant generator was trained to do the opposite, producing scans that would be classified as referable by the classifier from non-referable scans. The generated BAMs highlighted known pathologic features including nonperfusion area and retinal fluid. A fully interpretable classifier based on these highlights could help clinicians better utilize and verify automated DR diagnosis.Comment: 12 pages, 8 figure

Neural Systems for Complex Identification Tasks: The Access Control System ZN-Face and the Alarm Identification SENECA for Steel Casting Processes

. Neural systems have become a widely used tool in many industrial and commercial applications. We report in this contribution on new applications of diagnosis and identification systems in two areas. Firstly, we present the access control system ZNFace, a product developped at the ZN, which makes automated face recognition available for commercial access control systems. Neural systems have the necessary flexibility to analyse the visual information correctly, which we perceive so easily as a person's face. ZN-Face combines high recognition rates (99 %) with fast computation (3 sec) on standard hardware components (PC). The system is integrated into an easy-to-use GUI. ZNFace is in daily operation at a large company since summer 1995. -- Secondly, we report on a neural system for industrial process diagnosis, namely alarm identification in continuous steel casting, where a neural net in combination with a fuzzy system detects dangerous "hot spot"-process faults (breakouts) from the ob..

IPPI - Integrierte Produkt- und Prozessinnovation - aktuell 2: Analysen, Instrumente, Methoden

SIGLEAvailable from TIB Hannover: QN 290(2)+a / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekBundesministerium fuer Bildung und Forschung (BMBF), Bonn (Germany)DEGerman

Deep-Learning–Aided Diagnosis of Diabetic Retinopathy, Age-Related Macular Degeneration, and Glaucoma Based on Structural and Angiographic OCT

Purpose: Timely diagnosis of eye diseases is paramount to obtaining the best treatment outcomes. OCT and OCT angiography (OCTA) have several advantages that lend themselves to early detection of ocular pathology; furthermore, the techniques produce large, feature-rich data volumes. However, the full clinical potential of both OCT and OCTA is stymied when complex data acquired using the techniques must be manually processed. Here, we propose an automated diagnostic framework based on structural OCT and OCTA data volumes that could substantially support the clinical application of these technologies. Design: Cross sectional study. Participants: Five hundred twenty-six OCT and OCTA volumes were scanned from the eyes of 91 healthy participants, 161 patients with diabetic retinopathy (DR), 95 patients with age-related macular degeneration (AMD), and 108 patients with glaucoma. Methods: The diagnosis framework was constructed based on semisequential 3-dimensional (3D) convolutional neural networks. The trained framework classifies combined structural OCT and OCTA scans as normal, DR, AMD, or glaucoma. Fivefold cross-validation was performed, with 60% of the data reserved for training, 20% for validation, and 20% for testing. The training, validation, and test data sets were independent, with no shared patients. For scans diagnosed as DR, AMD, or glaucoma, 3D class activation maps were generated to highlight subregions that were considered important by the framework for automated diagnosis. Main Outcome Measures: The area under the curve (AUC) of the receiver operating characteristic curve and quadratic-weighted kappa were used to quantify the diagnostic performance of the framework. Results: For the diagnosis of DR, the framework achieved an AUC of 0.95 ± 0.01. For the diagnosis of AMD, the framework achieved an AUC of 0.98 ± 0.01. For the diagnosis of glaucoma, the framework achieved an AUC of 0.91 ± 0.02. Conclusions: Deep learning frameworks can provide reliable, sensitive, interpretable, and fully automated diagnosis of eye diseases. Financial Disclosure(s): Proprietary or commercial disclosure may be found after the references

Long-term safety of pirfenidone: results of the prospective, observational PASSPORT study

Real-world studies include a broader patient population for a longer duration than randomised controlled trials (RCTs) and can provide relevant insights for clinical practice. PASSPORT was a multicentre, prospective, post-authorisation study of patients who were newly prescribed pirfenidone and followed for 2 years after initiating treatment. Physicians collected data on adverse drug reactions (ADRs), serious ADRs (SADRs) and ADRs of special interest (ADRSI) at baseline and then every 3 months. Post hoc stepwise logistic regression models were used to identify baseline characteristics associated with discontinuing treatment due to an ADR. Patients (n=1009, 99.7% with idiopathic pulmonary fibrosis) had a median pirfenidone exposure of 442.0 days. Overall, 741 (73.4%) patients experienced ADRs, most commonly nausea (20.6%) and fatigue (18.5%). ADRs led to treatment discontinuation in 290 (28.7%) patients after a median of 99.5 days. Overall, 55 (5.5%) patients experienced SADRs, with a fatal outcome in six patients. ADRSI were reported in 693 patients, most commonly gastrointestinal symptoms (38.3%) and photosensitivity reactions/skin rashes (29.0%). Older age and female sex were associated with early treatment discontinuation due to an ADR. Findings were consistent with the known safety profile of pirfenidone, based on RCT data and other post-marketing experience, with no new safety signals observed