Black Box Integration of Computer-Aided Diagnosis into PACS Deserves a Second Chance: Results of a Usability Study Concerning Bone Age Assessment

Geldermann, Ina; Grouls, Christoph; Kuhl, Christiane K.; Deserno, Thomas M.; Spreckelsen, Cord

doi:10.1007/s10278-013-9590-y

Cited by 10 publications

(3 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At the same time, it was demonstrated that there is a need to speed up the image processing tasks and avoid system failures while it was suggested that the information quality and user interface of systems be improved by using appropriate analysis and needs assessment of the end users. Last, a usability study on integrating CAD to PACS reported numerous weaknesses that users considered important in the context of the integrative workflow such as efficient handling and fast computation [50].…”

Section: Usability -For Effective and Beneficial Ai In Medical Imagingmentioning

confidence: 99%

FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging

Lekadir,

Osuala,

Gallin

et al. 2021

Preprint

View full text Add to dashboard Cite

The recent advancements in artificial intelligence (AI) combined with the extensive amount of data generated by today's clinical systems, has led to the development of imaging AI solutions across the whole value chain of medical imaging, including image reconstruction, medical image segmentation, image-based diagnosis and treatment planning. Notwithstanding the successes and future potential of AI in medical imaging, many stakeholders are concerned of the potential risks and ethical implications of imaging AI solutions, which are perceived as complex, opaque, and difficult to comprehend, utilise, and trust in critical clinical applications. Despite these concerns and risks, there are currently no concrete guidelines and best practices for guiding future AI developments in medical imaging towards increased trust, safety and adoption. To bridge this gap, this paper introduces a careful selection of guiding principles drawn from the accumulated experiences, consensus, and best practices from five large European projects on AI in Health Imaging. These guiding principles are named FUTURE-AI and its building blocks consist of (i) Fairness, (ii) Universality, (iii) Traceability, (iv) Usability, (v) Robustness and (vi) Explainability. In a step-by-step approach, these guidelines are further translated into a framework of concrete recommendations for specifying, developing, evaluating, and deploying technically, clinically and ethically trustworthy AI solutions into clinical practice.

show abstract

Section: Usability -For Effective and Beneficial Ai In Medical Imagingmentioning

confidence: 99%

FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging

Lekadir,

Osuala,

Gallin

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…However, medical images are blurry and distorted, making them more challenging to process than other image types [2]. Biomedical image analysis and processing have recently become an important research topic because of the difficulty in doing so quickly and precisely [3,4]. Emphysema, tuberculosis, edema, aspiration pneumonia, pneumothorax, lung cancer, and recently COVID-19 are all lung diseases that can cause breathing difficulties or ARDS (acute respiratory distress syndrome).…”

Section: Introductionmentioning

confidence: 99%

CX-Net: an efficient ensemble semantic deep neural network for ROI identification from chest-x-ray images for COPD diagnosis

Agughasi

Murali

2023

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

Automatic identification of salient features in large medical datasets, particularly in chest X-ray (CXR) images, is a crucial research area. Accurately detecting critical findings such as emphysema, pneumothorax, and chronic bronchitis can aid radiologists in prioritizing time-sensitive cases and screening for abnormalities. However, traditional deep neural network approaches often require bounding box annotations, which can be time-consuming and challenging to obtain. This study proposes an explainable ensemble learning approach, CX-Net, for lung segmentation and diagnosing lung disorders using CXR images.We compare four state-of-the-art convolutional neural network (CNN) models, including FPN, U-Net, LinkNet, and a customized U-Net model with ImageNet feature extraction, data augmentation, and dropout regularizations. All models are trained on the Montgomery and VinDR-CXR datasets with and without segmented ground-truth masks. To achieve model explainability, we integrate SHapley Additive exPlanations (SHAP) and Gradient-weighted Class Activation Mapping (Grad-CAM) techniques, which enable a better understanding of the decision-making process and provide visual explanations of critical regions within the CXR images. By employing ensembling, our outlier-resistant CX-Net achieves superior performance in lung segmentation, with Jaccard overlap similarity (JS) of 0.992, Dice coefficients (DC) of 0.994, precision (PPV) of 0.993, recall of 0.980, and accuracy of 0.976. The proposed approach demonstrates strong generalization capabilities on the VinDr-CXR dataset and is the first study to use these datasets for semantic lung segmentation with semi-supervised localization. In conclusion, this paper presents an explainable ensemble learning approach for lung segmentation and diagnosing lung disorders using CXR images. Extensive experimental results show that our method efficiently and accurately extracts regions of interest in CXR images from publicly available datasets, indicating its potential for integration into clinical decision support systems (CDSS). Furthermore, incorporating SHAP and Grad-CAM techniques further enhances the interpretability and trustworthiness of the AI-driven diagnostic system.

show abstract

“…Other usability studies in radiology do provide more detailed qualitative usability information [10][11][12][13]. However, three of these studies [10,12,13] evaluated systems that do not play a critical role in the radiological workflow and one [11] only performed a heuristic evaluation, which means that no actual users took part in the evaluation. Also, all of these studies were conducted in laboratory settings, so they do not provide information about usability issues encountered in clinical practice.…”

Section: Introductionmentioning

confidence: 99%

Post-deployment usability evaluation of a radiology workstation

Jorritsma

Cnossen

Dierckx

et al. 2016

International Journal of Medical Informatics

View full text Add to dashboard Cite

Black Box Integration of Computer-Aided Diagnosis into PACS Deserves a Second Chance: Results of a Usability Study Concerning Bone Age Assessment

Cited by 10 publications

References 37 publications

FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging

FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging

CX-Net: an efficient ensemble semantic deep neural network for ROI identification from chest-x-ray images for COPD diagnosis

Post-deployment usability evaluation of a radiology workstation

Contact Info

Product

Resources

About