Explainable AI for Glaucoma Prediction Analysis to Understand Risk Factors in Treatment Planning

Kamal, Masud; Dey, Nilanjan; Chowdhury, Linkon; Hasan, Syed Irtija; Santosh, K. C.

doi:10.1109/tim.2022.3171613

Cited by 49 publications

(14 citation statements)

References 48 publications

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“…Chayan et al 37 proposed that providing explainability through LIME would provide medical professionals with comprehensive information to make decisions and thus would build their trust in the DL model. Kamal et al 36 proposed another model, submodular pick LIME (SP-LIME), that explained the predictive results and associated risk factors for the determination of glaucoma class. They claimed their model allowed clinicians to better understand the decision-making process and obtain convincing and consistent decisions 36 …”

Section: Resultsmentioning

confidence: 99%

“…Kamal et al 36 proposed another model, submodular pick LIME (SP-LIME), that explained the predictive results and associated risk factors for the determination of glaucoma class. They claimed their model allowed clinicians to better understand the decision-making process and obtain convincing and consistent decisions 36 …”

Section: Resultsmentioning

confidence: 99%

“…They claimed their model allowed clinicians to better understand the decision-making process and obtain convincing and consistent decisions. 36 Another form of explainable AI, the Shapley value, is a formula derived from a novel solution concept, Cooperative Game Theory. 62 It takes into account all the contributions other players make when interacting with a player and is considered the standard for quantifying an instance's contribution.…”

Section: Explainable Artificial Intelligence For Tabular Datamentioning

confidence: 99%

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Review of Visualization Approaches in Deep Learning Models of Glaucoma

Sidhu

Weinreb

et al. 2023

Asia-Pacific Journal of Ophthalmology

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Glaucoma is a major cause of irreversible blindness worldwide. As glaucoma often presents without symptoms, early detection and intervention are important in delaying progression. Deep learning (DL) has emerged as a rapidly advancing tool to help achieve these objectives. In this narrative review, data types and visualization approaches for presenting model predictions, including models based on tabular data, functional data, and/or structural data, are summarized, and the importance of data source diversity for improving the utility and generalizability of DL models is explored. Examples of innovative approaches to understanding predictions of artificial intelligence (AI) models and alignment with clinicians are provided. In addition, methods to enhance the interpretability of clinical features from tabular data used to train AI models are investigated. Examples of published DL models that include interfaces to facilitate end-user engagement and minimize cognitive and time burdens are highlighted. The stages of integrating AI models into existing clinical workflows are reviewed, and challenges are discussed. Reviewing these approaches may help inform the generation of user-friendly interfaces that are successfully integrated into clinical information systems. This review details key principles regarding visualization approaches in DL models of glaucoma. The articles reviewed here focused on usability, explainability, and promotion of clinician trust to encourage wider adoption for clinical use. These studies demonstrate important progress in addressing visualization and explainability issues required for successful real-world implementation of DL models in glaucoma.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Explainable Artificial Intelligence For Tabular Datamentioning

confidence: 99%

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Review of Visualization Approaches in Deep Learning Models of Glaucoma

Sidhu

Weinreb

et al. 2023

Asia-Pacific Journal of Ophthalmology

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“…To gain further insights into the decision-making process of the ML algorithms, we employed XAI techniques, specifically utilizing the SHapley Additive exPlanations (SHAP) method [125,126]. By leveraging SHAP, we were able to delve into the impact of different features on the classification outcomes, enhancing our understanding of species-specific information and the distinctive effects of individual features on each species.…”

Section: Explainable Artificial Intelligencementioning

confidence: 99%

GeneAI 3.0: Powerful, Novel, Generalized Hybrid and Ensemble Deep Learning Frameworks for miRNA Classification of species-specific Stationary Patterns from Nucleotides

Singh

Khanna

Rout

et al. 2023

Preprint

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Background and Motivation: Due to the intricate relationship between the small non-coding ribonucleic acid (miRNA) sequences, the classification of miRNA species, namely Human, Gorilla, Rat, and Mouse is challenging. Previous methods are not robust and accurate. In this study, we present GeneAI 3.0 (AtheroPoint™, Roseville, CA, USA), a powerful, novel, and generalized method for extracting features from the fixed patterns of purines and pyrimidines in each miRNA sequence in ensemble paradigms in machine learning (EML) and convolutional neural network (CNN)-based deep learning (EDL) frameworks. Method: GeneAI 3.0 utilized five conventional (Entropy, Dissimilarity, Energy, Homogeneity, and Contrast), and three contemporary (Shannon entropy, Hurst exponent, Fractal dimension) features, to generate a compositefeature set from given miRNA sequences which were then passed into our ML and DL classification framework. A set of 11 new classifiers was designed consisting of five EML and six EDL for binary/multiclass classification. It was benchmarked against 9 solo ML (SML), 6 solo DL (SDL), 12 hybrid DL (HDL) models, resulting in a total of 11+27=38 models were designed. Four hypotheses were formulated and validated using explainable AI (XAI) as well as reliability/statistical tests. Results: The order of the mean performance using accuracy (ACC)/area-under-the-curve (AUC) of the 24 DL classifiers was: EDL>HDL>SDL. The mean performance of EDL models with CNN layers was superior to that without CNN layers by 0.73%/0.92%. Mean performance of EML models was superior to SML models with improvements of ACC/AUC by 6.24%/6.46%. EDL models performed significantly better than EML models, with a mean increase in ACC/AUC of 7.09%/6.96%. The GeneAI 3.0 tool produced expected XAI feature plots, and the statistical tests showed significant p-values. Conclusions: Ensemble models with composite features are highly effective and generalized models for effectively classifying miRNA sequences.

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“…The robust XAI or explainable machine learning ( XML) methods LIME [47] and SHAP [48] methods were used in various fields and were certificated efficient, especially in medical and clinical areas. Some research confirmed that LIME [49][50][51][52] and SHAP [51][52][53][54][55] could be used to explain models and give reasons for model decisions. Because of the robustness of XML methods, we used them to analyse our data and acquired perfect/hoped results.…”

Section: Introductionmentioning

confidence: 99%

Interpretable machine learning analysis to identify risk factors for diabetes using the anonymous living census data of Japan

2023

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Purpose Diabetes mellitus causes various problems in our life. With the big data boom in our society, some risk factors for Diabetes must still exist. To identify new risk factors for diabetes in the big data society and explore further efficient use of big data, the non-objective-oriented census data about the Japanese Citizen’s Survey of Living Conditions were analyzed using interpretable machine learning methods. Methods Seven interpretable machine learning methods were used to analysis Japan citizens’ census data. Firstly, logistic analysis was used to analyze the risk factors of diabetes from 19 selected initial elements. Then, the linear analysis, linear discriminate analysis, Hayashi’s quantification analysis method 2, random forest, XGBoost, and SHAP methods were used to re-check and find the different factor contributions. Finally, the relationship among the factors was analyzed to understand the relationship among factors. Results Four new risk factors: the number of family members, insurance type, public pension type, and health awareness level, were found as risk factors for diabetes mellitus for the first time, while another 11 risk factors were reconfirmed in this analysis. Especially the insurance type factor and health awareness level factor make more contributions to diabetes than factors: hypertension, hyperlipidemia, and stress in some interpretable models. We also found that work years were identified as a risk factor for diabetes because it has a high coefficient with the risk factor of age. Conclusions New risk factors for diabetes mellitus were identified based on Japan's non-objective-oriented anonymous census data using interpretable machine learning models. The newly identified risk factors inspire new possible policies for preventing diabetes. Moreover, our analysis certifies that big data can help us find helpful knowledge in today's prosperous society. Our study also paves the way for identifying more risk factors and promoting the efficiency of using big data.

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Explainable AI for Glaucoma Prediction Analysis to Understand Risk Factors in Treatment Planning

Cited by 49 publications

References 48 publications

Review of Visualization Approaches in Deep Learning Models of Glaucoma

Review of Visualization Approaches in Deep Learning Models of Glaucoma

GeneAI 3.0: Powerful, Novel, Generalized Hybrid and Ensemble Deep Learning Frameworks for miRNA Classification of species-specific Stationary Patterns from Nucleotides

Interpretable machine learning analysis to identify risk factors for diabetes using the anonymous living census data of Japan

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