Deep learning algorithm predicts diabetic retinopathy progression in individual patients

Arcadu, Filippo; Benmansour, Fethallah; Maunz, Andreas; Willis, Jeff; Hašková, Zdenka; Prunotto, Marco

doi:10.1038/s41746-019-0172-3

Cited by 237 publications

(144 citation statements)

References 32 publications

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“…Finally, a recent subject of application for CNN architectures is the analysis of ophthalmology images, e.g. for prediction of prognosis in diabetic retinopathy [19] or macular edema classification [20].…”

Section: Plos Onementioning

confidence: 99%

A hybrid unsupervised—Deep learning tandem for electrooculography time series analysis

Stoean

Becerra-García

et al. 2020

PLoS ONE

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Medical data are often tricky to get mined for patterns even by the generally demonstrated successful modern methodologies of deep learning. This paper puts forward such a medical classification task, where patient registers of two of the categories are sometimes hard to be distinguished because of samples showing characteristics of both labels in turn in several repetitions of the screening procedure. To this end, the current research appoints a pre-processing clustering step (through self-organizing maps) to group the data based on shape similarity and relabel it accordingly. Subsequently, a deep learning approach (a tandem of convolutional and long short-term memory networks) performs the training classification phase on the 'cleaned' samples. The dual methodology was applied for the computational diagnosis of electrooculography tests within spino-cerebral ataxia of type 2. The accuracy obtained for the discrimination into three classes was of 78.24%. The improvement that this duo brings over the deep learner alone does not stem from significantly higher accuracy results when the performance is considered for all classes. The major finding of this combination is that half of the presymptomatic cases were correctly found, in opposition to the single deep model, where this category was sacrificed by the learner in favor of a good accuracy overall. A high accuracy in general is desirable for any medical task, however the correct identification of cases before the symptoms become evident is more important.

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Section: Plos Onementioning

confidence: 99%

A hybrid unsupervised—Deep learning tandem for electrooculography time series analysis

Stoean

Becerra-García

et al. 2020

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Second, the SSS relies on two hand-crafted features and cannot receive other potentially risk-determining features. Recent work applying deep learning (DL) 14 has shown promise in the automated diagnosis and triage of conditions including cardiac, pediatric, dermatological, and retinal diseases 13,[15][16][17][18][19][20][21][22][23][24][25][26] , but not in predicting the risk of AMD progression on a large scale or at the patient level 27 . Specifically, Burlina et al reported on the use of DL for estimating the AREDS 9-step severity grades of individual eyes, based on CFP in the AREDS data set [27][28][29] .…”

Section: Introductionmentioning

confidence: 99%

Predicting risk of late age-related macular degeneration using deep learning

et al. 2020

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By 2040, age-related macular degeneration (AMD) will affect~288 million people worldwide. Identifying individuals at high risk of progression to late AMD, the sight-threatening stage, is critical for clinical actions, including medical interventions and timely monitoring. Although deep learning has shown promise in diagnosing/screening AMD using color fundus photographs, it remains difficult to predict individuals' risks of late AMD accurately. For both tasks, these initial deep learning attempts have remained largely unvalidated in independent cohorts. Here, we demonstrate how deep learning and survival analysis can predict the probability of progression to late AMD using 3298 participants (over 80,000 images) from the Age-Related Eye Disease Studies AREDS and AREDS2, the largest longitudinal clinical trials in AMD. When validated against an independent test data set of 601 participants, our model achieved high prognostic accuracy (5-year C-statistic 86.4 (95% confidence interval 86.2-86.6)) that substantially exceeded that of retinal specialists using two existing clinical standards (81.3 (81.1-81.5) and 82.0 (81.8-82.3), respectively). Interestingly, our approach offers additional strengths over the existing clinical standards in AMD prognosis (e.g., risk ascertainment above 50%) and is likely to be highly generalizable, given the breadth of training data from 82 US retinal specialty clinics. Indeed, during external validation through training on AREDS and testing on AREDS2 as an independent cohort, our model retained substantially higher prognostic accuracy than existing clinical standards. These results highlight the potential of deep learning systems to enhance clinical decision-making in AMD patients.

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“…Given the potentially high number of radiomics features created on top of a rising number of clinical variables, powerful algorithms are needed to encompass and make all available data flourish. This is where ML algorithms thrive and have already shown tremendous results for a number of malignancies [42][43][44], but have, for now, barely been explored in ASCC. As an exception, using various ML algorithms including random forest and J48 decision trees, De Bari et al created a model predicting inguinal relapse with respective sensitivity, specificity and accuracy of 86.4%, 50% and 83.1% on the validation dataset (and superior results compared to logistic regression), highlighting the potential of such algorithms for ASCC care [45].…”

Section: Discussionmentioning

confidence: 99%

Radiomics and Machine Learning in Anal Squamous Cell Carcinoma: A New Step for Personalized Medicine?

et al. 2020

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Anal squamous cell carcinoma (ASCC) is an uncommon yet rising cancer worldwide. Definitive chemo-radiation (CRT) remains the best curative treatment option for non-metastatic cases in terms of local control, recurrence-free and progression-free survival. Still, despite overall good results, with 80% five-year survival, a subgroup of ASCC patients displays a high level of locoregional and/or metastatic recurrence rates, up to 35%, and may benefit from a more aggressive strategy. Beyond initial staging, there is no reliable marker to predict recurrence following CRT. Imaging, mostly positron emission tomography-computed tomography (PET-CT) and magnetic resonance imaging (MRI), bears an important role in the diagnosis and follow-up of ASCC. The routine use of radiomics may enhance the quality of information derived from these modalities. It is thought that including data derived from radiomics into the input flow of machine learning algorithms may improve the prediction of recurrence. Although some studies have shown glimmers of hope, more data is needed before offering practitioners tools to identify high-risk patients and enable extensive clinical application, especially regarding the matters of imaging normalization, radiomics process standardization and access to larger patient databases with external validation in order to allow results extrapolation. The aim of this review is to present a critical overview from this data.

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Deep learning algorithm predicts diabetic retinopathy progression in individual patients

Cited by 237 publications

References 32 publications

A hybrid unsupervised—Deep learning tandem for electrooculography time series analysis

A hybrid unsupervised—Deep learning tandem for electrooculography time series analysis

Predicting risk of late age-related macular degeneration using deep learning

Radiomics and Machine Learning in Anal Squamous Cell Carcinoma: A New Step for Personalized Medicine?

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