Replication and Refinement of an Algorithm for Automated Drusen Segmentation on Optical Coherence Tomography

Here, we investigate the extent to which re-implementing a previously published algorithm for OCT-based drusen quantification permits replicating the reported accuracy on an independent dataset. We refined that algorithm so that its accuracy is increased. Following a systematic literature search, an algorithm was selected based on its reported excellent results. Several steps were added to improve its accuracy. The replicated and refined algorithms were evaluated on an independent dataset with the same metrics as in the original publication. Accuracy of the refined algorithm (overlap ratio 36–52%) was significantly greater than the replicated one (overlap ratio 25–39%). In particular, separation of the retinal pigment epithelium and the ellipsoid zone could be improved by the refinement. However, accuracy was still lower than reported previously on different data (overlap ratio 67–76%). This is the first replication study of an algorithm for OCT image analysis. Its results indicate that current standards for algorithm validation do not provide a reliable estimate of algorithm performance on images that differ with respect to patient selection and image quality. In order to contribute to an improved reproducibility in this field, we publish both our replication and the refinement, as well as an exemplary dataset

Comparability of automated drusen volume measurements in age-related macular degeneration: a MACUSTAR study report

Drusen are hallmarks of early and intermediate age-related macular degeneration (AMD) but their quantification remains a challenge. We compared automated drusen volume measurements between different OCT devices. We included 380 eyes from 200 individuals with bilateral intermediate (iAMD, n = 126), early (eAMD, n = 25) or no AMD (n = 49) from the MACUSTAR study. We assessed OCT scans from Cirrus (200 × 200 macular cube, 6 × 6 mm; Zeiss Meditec, CA) and Spectralis (20° × 20°, 25 B-scans; 30° × 25°, 241 B-scans; Heidelberg Engineering, Germany) devices. Sensitivity and specificity for drusen detection and differences between modalities were assessed with intra-class correlation coefficients (ICCs) and mean difference in a 5 mm diameter fovea-centered circle. Specificity was > 90% in the three modalities. In eAMD, we observed highest sensitivity in the denser Spectralis scan (68.1). The two different Spectralis modalities showed a significantly higher agreement in quantifying drusen volume in iAMD (ICC 0.993 [0.991–0.994]) than the dense Spectralis with Cirrus scan (ICC 0.807 [0.757–0.847]). Formulae for drusen volume conversion in iAMD between the two devices are provided. Automated drusen volume measures are not interchangeable between devices and softwares and need to be interpreted with the used imaging devices and software in mind. Accounting for systematic difference between methods increases comparability and conversion formulae are provided. Less dense scans did not affect drusen volume measurements in iAMD but decreased sensitivity for medium drusen in eAMD. Trial registration: ClinicalTrials.gov NCT03349801. Registered on 22 November 2017

Comparability of automated drusen volume measurements in age-related macular degeneration: a MACUSTAR study report

Drusen are hallmarks of early and intermediate age-related macular degeneration (AMD) but their quantification remains a challenge. We compared automated drusen volume measurements between different OCT devices. We included 380 eyes from 200 individuals with bilateral intermediate (iAMD, n = 126), early (eAMD, n = 25) or no AMD (n = 49) from the MACUSTAR study. We assessed OCT scans from Cirrus (200 × 200 macular cube, 6 × 6 mm; Zeiss Meditec, CA) and Spectralis (20° × 20°, 25 B-scans; 30° × 25°, 241 B-scans; Heidelberg Engineering, Germany) devices. Sensitivity and specificity for drusen detection and differences between modalities were assessed with intra-class correlation coefficients (ICCs) and mean difference in a 5 mm diameter fovea-centered circle. Specificity was > 90% in the three modalities. In eAMD, we observed highest sensitivity in the denser Spectralis scan (68.1). The two different Spectralis modalities showed a significantly higher agreement in quantifying drusen volume in iAMD (ICC 0.993 [0.991–0.994]) than the dense Spectralis with Cirrus scan (ICC 0.807 [0.757–0.847]). Formulae for drusen volume conversion in iAMD between the two devices are provided. Automated drusen volume measures are not interchangeable between devices and softwares and need to be interpreted with the used imaging devices and software in mind. Accounting for systematic difference between methods increases comparability and conversion formulae are provided. Less dense scans did not affect drusen volume measurements in iAMD but decreased sensitivity for medium drusen in eAMD

Comparability of automated drusen volume measurements in age-related macular degeneration: a MACUSTAR study report

Drusen are hallmarks of early and intermediate age-related macular degeneration (AMD) but their quantification remains a challenge. We compared automated drusen volume measurements between different OCT devices. We included 380 eyes from 200 individuals with bilateral intermediate (iAMD, n = 126), early (eAMD, n = 25) or no AMD (n = 49) from the MACUSTAR study. We assessed OCT scans from Cirrus (200 × 200 macular cube, 6 × 6 mm; Zeiss Meditec, CA) and Spectralis (20° × 20°, 25 B-scans; 30° × 25°, 241 B-scans; Heidelberg Engineering, Germany) devices. Sensitivity and specificity for drusen detection and differences between modalities were assessed with intra-class correlation coefficients (ICCs) and mean difference in a 5 mm diameter fovea-centered circle. Specificity was > 90% in the three modalities. In eAMD, we observed highest sensitivity in the denser Spectralis scan (68.1). The two different Spectralis modalities showed a significantly higher agreement in quantifying drusen volume in iAMD (ICC 0.993 [0.991-0.994]) than the dense Spectralis with Cirrus scan (ICC 0.807 [0.757-0.847]). Formulae for drusen volume conversion in iAMD between the two devices are provided. Automated drusen volume measures are not interchangeable between devices and softwares and need to be interpreted with the used imaging devices and software in mind. Accounting for systematic difference between methods increases comparability and conversion formulae are provided. Less dense scans did not affect drusen volume measurements in iAMD but decreased sensitivity for medium drusen in eAMD

Artificial intelligence for clinical decision support for monitoring patients in cardiovascular ICUs: a systematic review

Background: Artificial intelligence (AI) and machine learning (ML) models continue to evolve the clinical decision support systems (CDSS). However, challenges arise when it comes to the integration of AI/ML into clinical scenarios. In this systematic review, we followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA), the population, intervention, comparator, outcome, and study design (PICOS), and the medical AI life cycle guidelines to investigate studies and tools which address AI/ML-based approaches towards clinical decision support (CDS) for monitoring cardiovascular patients in intensive care units (ICUs). We further discuss recent advances, pitfalls, and future perspectives towards effective integration of AI into routine practices as were identified and elaborated over an extensive selection process for state-of-the-art manuscripts. Methods: Studies with available English full text from PubMed and Google Scholar in the period from January 2018 to August 2022 were considered. The manuscripts were fetched through a combination of the search keywords including AI, ML, reinforcement learning (RL), deep learning, clinical decision support, and cardiovascular critical care and patients monitoring. The manuscripts were analyzed and filtered based on qualitative and quantitative criteria such as target population, proper study design, cross-validation, and risk of bias. Results: More than 100 queries over two medical search engines and subjective literature research were developed which identified 89 studies. After extensive assessments of the studies both technically and medically, 21 studies were selected for the final qualitative assessment. Discussion: Clinical time series and electronic health records (EHR) data were the most common input modalities, while methods such as gradient boosting, recurrent neural networks (RNNs) and RL were mostly used for the analysis. Seventy-five percent of the selected papers lacked validation against external datasets highlighting the generalizability issue. Also, interpretability of the AI decisions was identified as a central issue towards effective integration of AI in healthcare

Automated Analysis of Retinal and Choroidal OCT and OCTA Images in AMD

La dégénérescence maculaire liée à l'âge (DMLA) est une maladie oculaire progressive qui se manifeste principalement au niveau de la rétine externe et de la choroïde. Le projet de recherche vise à déterminer si des mesures obtenues à partir d'images de tomographie par cohérence optique (OCT) et d'angiographie OCT (OCTA) peuvent être utilisées afin de fournir de nouvelles informations sur des biomarqueurs de la DMLA, ainsi qu’une méthode de détection précoce de la maladie. À cette fin, un appareil permettant l’OCT et l’OCTA a été utilisé pour imager des sujets DMLA précoces et intermédiaires, et des sujets témoins. À la configuration sélectionnée de l’appareil OCT, chaque acquisition d'un œil fournit un volume de données qui est constitué de 300 images transversales appelées B-scan. Au total, des acquisitions de 10 yeux de sujets atteints de DMLA précoce et intermédiaire (3000 images B-scan) et un cas de DMLA néovasculaire, 12 yeux de sujets âgés de plus de 50 ans (3600 images B-scan) et 11 yeux de sujets âgés de moins de 50 ans (3300 images B-scan) ont été obtenues. Cinq méthodes d'extraction de caractéristiques ont été reproduites ou développées afin de déterminer si des différences significatives au niveau de l’œil pouvaient être observées entre les sujets atteints de DMLA précoce et intermédiaire et les sujets témoins d’âge similaire. Grâce à des tests non paramétriques, il a été établi que deux méthodes connues d'extraction de biomarqueurs de la DMLA (analyse d’absence de signal de débit sanguin au niveau de la choriocapillaire et une méthode de segmentation des drusen) produisent des mesures qui montrent des différences significatives entre les groupes, et qui sont représentées de façon uniforme à travers le plan frontal de l’œil. Il a ensuite été souhaité de tirer parti des mesures et de générer un modèle de classification de la DMLA interprétable basé sur l'apprentissage automatique au niveau des B-scans. Des spectres de fréquence résultant de la transformé de Fourier rapide de séries spatiales dérivées de mesures considérées comme représentatives des deux biomarqueurs ont été obtenues, et utilisées comme caractéristiques pour former un classifieur de type forêt aléatoire et un classifieur de type forêt profonde. L'analyse en composantes principales (PCA) a été utilisée pour réduire la dimensionnalité de l’espace des caractéristiques, et la performance des modèles et l'importance des prédicteurs ont été évaluées. Une nouvelle méthode a été conçue qui permet une reconstruction 3D automatisée et une évaluation quantitative de la structure des signaux OCTA et ainsi des vaisseaux rétiniens. Des mesures représentatives des drusen et de la choriocapillaire ont été utilisées pour créer des modèles interprétables pour la classification de la DMLA précoce et intermédiaire. Alors que la prévalence mondiale de la DMLA augmente et que les appareils OCT deviennent plus disponibles, un plus grand nombre de personnes hautement qualifiées est nécessaire pour interpréter les informations médicales et fournir les soins cliniques appropriés. L'analyse et le classement du niveau de sévérité de la DMLA par des experts par le biais d'images OCT sont coûteux et prennent du temps. Les modèles proposés pourraient servir à automatiser la détection de la DMLA, même lorsqu'elle est asymptomatique, et signaler à un ophtalmologue la nécessité de surveiller et de traiter la condition avant la survenue de pertes graves de la vision. Les modèles sont transparents et sont en mesure de fournir une classification à partir d’une seule image transversale. Par conséquent, l'outil diagnostic automatisé pourrait également être utilisé dans des situations où seules des données médicales partielles sont disponibles ou lorsque l'accès aux ressources de soins de santé est limité.----------ABSTRACT Age-related macular degeneration (AMD) is a progressive eye disease which manifests primarily at the outer retina and choroid. The research project aimed to determine whether measures obtained from optical coherence tomography (OCT) and OCT angiography (OCTA) images could be used to provide novel AMD biomarker insight and an early disease detection method. To that end, an OCT and OCTA enabled device was used to image AMD subjects and controls. At the selected device scan size, each scan of one eye gathered using an OCT device provides a volume of data which is constructed of 300 cross-sectional images termed B-scans. In total, scans of 10 eyes from subjects with early and intermediate AMD (3,000 B-scan images) and a case of neovascular AMD, 12 eyes from subjects over the age of 50 years old (3,600 B-scan images), and 11 eyes from subjects under the age of 50 years old (3,300 B-scan images) were obtained. Five feature extraction methods were either reproduced or developed in order to determine if significant differences could be observed between the early and intermediate AMD subjects and control subjects at the eye level. Through non-parametric testing it was established that two AMD biomarker extraction methods (choriocapillaris flow voids analysis and a drusen segmentation method) produced measures which showed significant differences between groups, and which were also uniformly represented across the frontal plane of the eye. It was then desired to leverage the measures and generate a B-scan level, interpretable machine learning-based AMD classification model. Frequency spectrums resulting from the fast Fourier transforms of spatial series derived from measures believed to be representative of the two biomarkers were obtained and used as features to train a random forest and a deep forest classifier. Principal component analysis was used to reduce dimensionality of the feature space, and model performance and predictor importance were assessed. A new method was devised which allows automated 3D reconstruction and quantitative evaluation of retinal flow signal patterns and incidentally of retinal microvasculature. Measures representative of drusen and choriocapillaris were leveraged to create interpretable models for the classification of early and intermediate AMD. As the worldwide prevalence of AMD increases and OCT devices are becoming more available, a greater number of highly trained personnel is needed to interpret medical information and provide the appropriate clinical care. Expert analysis and grading of AMD through OCT images are expensive and time consuming. The models proposed could serve to automate AMD detection, even when it is asymptomatic, and signal to an ophthalmologist the need to monitor and treat the condition before the occurrence of severe visual loss. The models are transparent and provide classification from single cross-sectional images. Therefore, the automated diagnosis tool could also be used in situations where only partial medical data are available, or where there is limited access to health care resources