Dataset Information

Automatically Enhanced OCT Scans of the Retina: A proof of concept study.

ABSTRACT: In this work we evaluated a postprocessing, customized automatic retinal OCT B-scan enhancement software for noise reduction, contrast enhancement and improved depth quality applicable to Heidelberg Engineering Spectralis OCT devices. A trained deep neural network was used to process images from an OCT dataset with ground truth biomarker gradings. Performance was assessed by the evaluation of two expert graders who evaluated image quality for B-scan with a clear preference for enhanced over original images. Objective measures such as SNR and noise estimation showed a significant improvement in quality. Presence grading of seven biomarkers IRF, SRF, ERM, Drusen, RPD, GA and iRORA resulted in similar intergrader agreement. Intergrader agreement was also compared with improvement in IRF and RPD, and disagreement in high variance biomarkers such as GA and iRORA.

SUBMITTER: Apostolopoulos S

PROVIDER: S-EPMC7210925 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Automatically Enhanced OCT Scans of the Retina: A proof of concept study.

Apostolopoulos Stefanos S Salas Jazmín J Ordóñez José L P JLP Tan Shern Shiou SS Ciller Carlos C Ebneter Andreas A Zinkernagel Martin M Sznitman Raphael R Wolf Sebastian S De Zanet Sandro S Munk Marion R MR

Scientific reports 20200508 1

In this work we evaluated a postprocessing, customized automatic retinal OCT B-scan enhancement software for noise reduction, contrast enhancement and improved depth quality applicable to Heidelberg Engineering Spectralis OCT devices. A trained deep neural network was used to process images from an OCT dataset with ground truth biomarker gradings. Performance was assessed by the evaluation of two expert graders who evaluated image quality for B-scan with a clear preference for enhanced over orig ...[more]

PMID: 32385371

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Project description:BackgroundValue sets are lists of terms (e.g., opioid medication names) and their corresponding codes from standard clinical vocabularies (e.g., RxNorm) created with the intent of supporting health information exchange and research. Value sets are manually-created and often exhibit errors.ObjectivesThe aim of the study is to develop a semi-automatic, data-centric natural language processing (NLP) method to assess medication-related value set correctness and evaluate it on a set of opioid medication value sets.MethodsWe developed an NLP algorithm that utilizes value sets containing mostly true positives and true negatives to learn lexical patterns associated with the true positives, and then employs these patterns to identify potential errors in unseen value sets. We evaluated the algorithm on a set of opioid medication value sets, using the recall, precision and F1-score metrics. We applied the trained model to assess the correctness of unseen opioid value sets based on recall. To replicate the application of the algorithm in real-world settings, a domain expert manually conducted error analysis to identify potential system and value set errors.ResultsThirty-eight value sets were retrieved from the Value Set Authority Center, and six (two opioid, four non-opioid) were used to develop and evaluate the system. Average precision, recall, and F1-score were 0.932, 0.904, and 0.909, respectively on uncorrected value sets; and 0.958, 0.953, and 0.953, respectively after manual correction of the same value sets. On 20 unseen opioid value sets, the algorithm obtained average recall of 0.89. Error analyses revealed that the main sources of system misclassifications were differences in how opioids were coded in the value sets-while the training value sets had generic names mostly, some of the unseen value sets had new trade names and ingredients.ConclusionThe proposed approach is data-centric, reusable, customizable, and not resource intensive. It may help domain experts to easily validate value sets.

Dataset Information

Automatically Enhanced OCT Scans of the Retina: A proof of concept study.

Publications

Automatically Enhanced OCT Scans of the Retina: A proof of concept study.

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