RETRACTED ARTICLE: Prediction of atherosclerosis pathology in retinal fundal images with machine learning approaches

Parameswari, C.; Ranjani, S. Siva

doi:10.1007/s12652-020-02294-3

Cited by 13 publications

(7 citation statements)

References 34 publications

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“…One study did not demonstrate the enrollment process, putting it at high risk of selection bias. The time intervals between the point when ocular images were taken and the point when diagnoses were made in 3 studies were not indicated [21][22][23] ; the interval was critical in the study predicting coronary artery disease (CAD), which may lead to bias as CAD changes are time-dependent. 21 In the other 29 studies, chronic diseases diagnoses were made before ocular examination or at the same time using blood test results, which posed a low risk of bias.…”

Section: Resultsmentioning

confidence: 99%

“…Studies have shown that arterial atherosclerosis predisposed ischemic heart disease and stroke and was correlated to the progression of hypertension 29 . These findings led to the development of AI tools for identifying arterial atherosclerosis on the retina, 23 in which a high sensitivity of 89.1% was achieved 30 . However, the model was built on a dataset with a small sample size with limited diagnosis information, and its diagnostic power was not tested on external datasets.…”

Section: Discussionmentioning

confidence: 99%

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Detection of Systemic Diseases From Ocular Images Using Artificial Intelligence: A Systematic Review

Peng

Tseng

Tham

et al. 2022

Asia-Pacific Journal of Ophthalmology

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Purpose: Despite the huge investment in health care, there is still a lack of precise and easily accessible screening systems. With proven associations to many systemic diseases, the eye could potentially provide a credible perspective as a novel screening tool. This systematic review aims to summarize the current applications of ocular image–based artificial intelligence on the detection of systemic diseases and suggest future trends for systemic disease screening. Methods: A systematic search was conducted on September 1, 2021, using 3 databases—PubMed, Google Scholar, and Web of Science library. Date restrictions were not imposed and search terms covering ocular images, systemic diseases, and artificial intelligence aspects were used. Results: Thirty-three papers were included in this systematic review. A spectrum of target diseases was observed, and this included but was not limited to cardio-cerebrovascular diseases, central nervous system diseases, renal dysfunctions, and hepatological diseases. Additionally, one- third of the papers included risk factor predictions for the respective systemic diseases. Conclusions: Ocular image – based artificial intelligence possesses potential diagnostic power to screen various systemic diseases and has also demonstrated the ability to detect Alzheimer and chronic kidney diseases at early stages. Further research is needed to validate these models for real-world implementation.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Detection of Systemic Diseases From Ocular Images Using Artificial Intelligence: A Systematic Review

Peng

Tseng

Tham

et al. 2022

Asia-Pacific Journal of Ophthalmology

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“…When compared to traditional scores, ML-based models do not always improve performance in terms of accuracy [ 29 ]. However, several advantages may become apparent in the long term [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ]. Specifically, compared to the static nature of traditional scores, the performance of the RAIN-ML prediction model is dynamic, thanks to its evolutive learning feature allowing the model to improve its classification algorithm by learning strategies at the increased enrollment time and number of recruited patients.…”

Section: Discussionmentioning

confidence: 99%

Prediction of All-Cause Mortality Following Percutaneous Coronary Intervention in Bifurcation Lesions Using Machine Learning Algorithms

Burrello

Gallone

Burrello

et al. 2022

JPM

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Stratifying prognosis following coronary bifurcation percutaneous coronary intervention (PCI) is an unmet clinical need that may be fulfilled through the adoption of machine learning (ML) algorithms to refine outcome predictions. We sought to develop an ML-based risk stratification model built on clinical, anatomical, and procedural features to predict all-cause mortality following contemporary bifurcation PCI. Multiple ML models to predict all-cause mortality were tested on a cohort of 2393 patients (training, n = 1795; internal validation, n = 598) undergoing bifurcation PCI with contemporary stents from the real-world RAIN registry. Twenty-five commonly available patient-/lesion-related features were selected to train ML models. The best model was validated in an external cohort of 1701 patients undergoing bifurcation PCI from the DUTCH PEERS and BIO-RESORT trial cohorts. At ROC curves, the AUC for the prediction of 2-year mortality was 0.79 (0.74–0.83) in the overall population, 0.74 (0.62–0.85) at internal validation and 0.71 (0.62–0.79) at external validation. Performance at risk ranking analysis, k-center cross-validation, and continual learning confirmed the generalizability of the models, also available as an online interface. The RAIN-ML prediction model represents the first tool combining clinical, anatomical, and procedural features to predict all-cause mortality among patients undergoing contemporary bifurcation PCI with reliable performance.

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“…Through RL, the network parameters of DL are tuned to continuously optimize their own behavior strategies; its framework is shown in Figure 2. This method has become a new research hotspot in the field of artificial intelligence and has been applied in fields such as robot control [30][31][32], autonomous driving [33], and machine vision [34][35][36][37][38][39][40][41]. In this paper, the vehicle autonomous driving decision problem is modeled with a partially observable Markov decision process (POMDP) [42], and the autonomous driving strategy optimization problem is solved by identifying the optimal driving strategy of the POMDP.…”

Section: Modeling Of the Automatic Driving Strategy Optimization Problemmentioning

confidence: 99%

APF-DPPO: An Automatic Driving Policy Learning Method Based on the Artificial Potential Field Method to Optimize the Reward Function

Lin

Zhang

et al. 2022

Machines

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To address the difficulty of obtaining the optimal driving strategy under the condition of a complex environment and changeable tasks of vehicle autonomous driving, this paper proposes an end-to-end autonomous driving strategy learning method based on deep reinforcement learning. The ideas of target attraction and obstacle rejection of the artificial potential field method are introduced into the distributed proximal policy optimization algorithm, and the APF-DPPO learning model is established. To solve the range repulsion problem of the artificial potential field method, which affects the optimal driving strategy, this paper proposes a directional penalty function method that combines collision penalty and yaw penalty to convert the range penalty of obstacles into a single directional penalty, and establishes the vehicle motion collision model. Finally, the APF-DPPO learning model is selected to train the driving strategy for the virtual vehicle, and the transfer learning method is selected to verify the comparison experiment. The simulation results show that the completion rate of the virtual vehicle in the obstacle environment that generates penalty feedback is as high as 96.3%, which is 3.8% higher than the completion rate in the environment that does not generate penalty feedback. Under different reward functions, the method in this paper obtains the highest cumulative reward value within 500 s, which improves 69 points compared with the reward function method based on the artificial potential field method, and has higher adaptability and robustness in different environments. The experimental results show that this method can effectively improve the efficiency of autonomous driving strategy learning and control the virtual vehicle for autonomous driving behavior decisions, and provide reliable theoretical and technical support for real vehicles in autonomous driving decision-making.

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RETRACTED ARTICLE: Prediction of atherosclerosis pathology in retinal fundal images with machine learning approaches

Cited by 13 publications

References 34 publications

Detection of Systemic Diseases From Ocular Images Using Artificial Intelligence: A Systematic Review

Detection of Systemic Diseases From Ocular Images Using Artificial Intelligence: A Systematic Review

Prediction of All-Cause Mortality Following Percutaneous Coronary Intervention in Bifurcation Lesions Using Machine Learning Algorithms

APF-DPPO: An Automatic Driving Policy Learning Method Based on the Artificial Potential Field Method to Optimize the Reward Function

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