An explainable AI system for automated COVID-19 assessment and lesion categorization from CT-scans

Pennisi, Matteo; Kavasidis, Isaak; Spampinato, Concetto; Schininà, Vincenzo; Palazzo, Simone; Salanitri, Federica Proietto; Bellitto, Giovanni; Rundo, Francesco; Aldinucci, Marco; Cristofaro, Massimo; Campioni, Paolo; Pianura, Elisa; Stefano, Federica Di; Petrone, Ada; Albarello, Fabrizio; Ippolito, Giuseppe; Cuzzocrea, Salvatore; Conoci, Sabrina

doi:10.1016/j.artmed.2021.102114

Cited by 36 publications

(15 citation statements)

References 46 publications

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“…Application of a technique that explains the model's decision-making process could provide information on possible biases in the model and ways to improve it. Pennisi et al [90] achieved sensitivity and specificity of COVID-19 lesion categorization of over 90% using a combination of lung lobe segmentation followed by lesion classification. In addition, they also created a clinician-facing user interface to visualize model prediction.…”

Section: Image Segmentationmentioning

confidence: 99%

“…This includes understanding the factors influencing response variables in the real world, as illustrated in Haimovich et al [105] when they stated that ICU admission was not an ideal outcome variable due to site-specific and time-dependent patient admission requirements. Clinical input may also be obtained during and after model optimization via real-time expert feedback [106] and during implementation via expert-facing user interfaces [42]. In addition to web-based applications, visualizing sample clusters [67], [87] and feature importance metrics [33], [71], [80] can offer users without expertise in data analysis an option of understanding the decision-making process of otherwise obscure models.…”

Section: A Checklist For Ai-enabled Clinical Applicationsmentioning

confidence: 99%

“…Incorporate clinical insights [105] Small sample size Data imputation [32], [59], [114] Interactive user interface [42], [90], [94], [105] Bad data quality Artifact correction [64], [115] Clinical feedback [90], [106] Imbalanced classes Data augmentation [62], [65] Visualization of feature importance [33], [71], [80] Complex disease phenotype Multi-modality data [57], [116] Clustering analysis [67], [87] Data heterogeneity Data normalization [59] Decision tree [4], [32], [33] Lack of expert annotation Weakly supervised learning [51], [71], [87] Use multiple feature importance approaches [13], [41], [74] Unkown sources of signal Key feature extraction [37], [58], [69] Cross validation when comparing models [46], [105], [107], [110] Explanations unclear Pre-processing changes [67], [68] Use appropriate and robust performance metrics (AUROC, MCC) [110], [111] Data leakage Patient-level split [45]…”

Section: Suggestions Problems Solutionsmentioning

confidence: 99%

See 2 more Smart Citations

Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review

Giuste

Shi

Zhu

et al. 2023

IEEE Rev. Biomed. Eng.

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Despite the myriad peer-reviewed papers demonstrating novel Artificial Intelligence (AI)-based solutions to COVID-19 challenges during the pandemic, few have made a significant clinical impact, especially in diagnosis and disease precision staging. One major cause for such low impact is the lack of model transparency, significantly limiting the AI adoption in real clinical practice. To solve this problem, AI models need to be explained to users. Thus, we have conducted a comprehensive study of Explainable Artificial Intelligence (XAI) using PRISMA technology. Our findings suggest that XAI can improve model performance, instill trust in the users, and assist users in decisionmaking. In this systematic review, we introduce common XAI techniques and their utility with specific examples of their application. We discuss the evaluation of XAI results because it is an important step for maximizing the value of AI-based clinical decision support systems. Additionally, we present the traditional, modern, and advanced XAI models to demonstrate the evolution of novel techniques. Finally, we provide a best practice guideline that developers can refer to during the model experimentation. We also offer potential solutions with specific examples for common challenges in AI model experimentation. This comprehensive review, hopefully, can promote AI adoption in biomedicine and healthcare.

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Section: Image Segmentationmentioning

confidence: 99%

Section: A Checklist For Ai-enabled Clinical Applicationsmentioning

confidence: 99%

Section: Suggestions Problems Solutionsmentioning

confidence: 99%

See 1 more Smart Citation

Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review

Giuste

Shi

Zhu

et al. 2023

IEEE Rev. Biomed. Eng.

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“…Over the last years, research has focused on developing algorithms that provide explanations for AI predictions [1,15]. By now, these algorithms are increasingly employed in a growing number of practical use cases such as in manufacturing [55,59], medicine [47], or the hospitality industry [62]. Usually, XAI is utilized in scenarios that involve humans-in-the-loop processes.…”

Section: Related Workmentioning

confidence: 99%

A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Making

Schemmer,

Hemmer,

Nitsche

et al. 2022

Preprint

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Research in artificial intelligence (AI)-assisted decision-making is experiencing tremendous growth with a constantly rising number of studies evaluating the effect of AI with and without techniques from the field of explainable AI (XAI) on human decision-making performance. However, as tasks and experimental setups vary due to different objectives, some studies report improved user decisionmaking performance through XAI, while others report only negligible effects. Therefore, in this article, we present an initial synthesis of existing research on XAI studies using a statistical meta-analysis to derive implications across existing research. We observe a statistically positive impact of XAI on users' performance. Additionally, the first results indicate that human-AI decision-making tends to yield better task performance on text data. However, we find no effect of explanations on users' performance compared to sole AI predictions. Our initial synthesis gives rise to future research investigating the underlying causes and contributes to further developing algorithms that effectively benefit human decision-makers by providing meaningful explanations. CCS CONCEPTS• Human-centered computing → Empirical studies in HCI; • Computing methodologies → Artificial intelligence.

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“…Enormous research studies presented the robustness of these techniques for image segmentation [24]. Explainable artificial intelligence (XAI) is basically the integration of multiple AI models into an ergonomic GUI to assist radiologists in decision-making in order to improve understanding of COVID-19 [25]. e recent studies faced several challenges for the accurate classification of COVID-19 with normal chest X-ray images.…”

Section: Introductionmentioning

confidence: 99%

COVID-19 Classification from Chest X-Ray Images: A Framework of Deep Explainable Artificial Intelligence

Khan

Azhar

Ibrar

et al. 2022

Computational Intelligence and Neuroscience

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COVID-19 detection and classification using chest X-ray images is a current hot research topic based on the important application known as medical image analysis. To halt the spread of COVID-19, it is critical to identify the infection as soon as possible. Due to time constraints and the expertise of radiologists, manually diagnosing this infection from chest X-ray images is a difficult and time-consuming process. Artificial intelligence techniques have had a significant impact on medical image analysis and have also introduced several techniques for COVID-19 diagnosis. Deep learning and explainable AI have shown significant popularity among AL techniques for COVID-19 detection and classification. In this work, we propose a deep learning and explainable AI technique for the diagnosis and classification of COVID-19 using chest X-ray images. Initially, a hybrid contrast enhancement technique is proposed and applied to the original images that are later utilized for the training of two modified deep learning models. The deep transfer learning concept is selected for the training of pretrained modified models that are later employed for feature extraction. Features of both deep models are fused using improved canonical correlation analysis that is further optimized using a hybrid algorithm named Whale-Elephant Herding. Through this algorithm, the best features are selected and classified using an extreme learning machine (ELM). Moreover, the modified deep models are utilized for Grad-CAM visualization. The experimental process was conducted on three publicly available datasets and achieved accuracies of 99.1, 98.2, and 96.7%, respectively. Moreover, the ablation study was performed and showed that the proposed accuracy is better than the other methods.

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An explainable AI system for automated COVID-19 assessment and lesion categorization from CT-scans

Cited by 36 publications

References 46 publications

Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review

Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review

A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Making

COVID-19 Classification from Chest X-Ray Images: A Framework of Deep Explainable Artificial Intelligence

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