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Lesion Detection Assessment in the Liver: Standard vs Low Radiation Dose Using Varied Post-Processing Techniques

ABSTRACT: To compare 2 different image creation/processing techniques during a standard CT scan in order to "see" problems in the liver and learn which method provides better image quality. The techniques use new artificial intelligence software to decrease image noise, which helps the radiologist to evaluate.

DISEASE(S): Colorectal Carcinoma,Neoplasm Metastasis,Carcinoma,Liver Metastases,Diseases Of Liver,Colorectal Neoplasms,Colon Carcinoma,Liver Diseases

PROVIDER: 2244256 | ecrin-mdr-crc |

REPOSITORIES: ECRIN MDR

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Functional Optimization of Designer Cardiac Organoids Enabled by Machine Learning Techniques

Project description:Stem cell organoids are powerful models for studying organ development, disease modeling, drug screening, and regenerative medicine applications. The convergence of organoid technology, tissue engineering, and artificial intelligence (AI) could potentially enhance our understanding of the design principle for organoid engineering. In this study, we utilized micropatterning techniques to create a designer library of 230 cardiac organoids with 7 geometric designs (Circle 200, Circle 600, Circle 1000, Rectangle 1:1, Rectangle 1:4, Star 1:1, and Star 1:4). We employed manifold learning techniques to analyze single organoid heterogeneity based on 10 physiological parameters. We successfully clustered and refined our cardiac organoids based on their functional similarity using unsupervised machine learning approaches, thus elucidating unique functionalities associated with geometric designs. We also highlighted the critical role of calcium rising time in distinguishing organoids based on geometric patterns and clustering results. This innovative integration of organoid engineering and machine learning enhances our understanding of structure-function relationships in cardiac organoids, paving the way for more controlled and optimized organoid design.

2024-05-17 | GSE267438 | GEO

Comparison of Flat Colorectal Lesion Detection by Artificial Intelligence-assisted Colonoscopy Versus Endoscopists

Project description:The development of artificial intelligence (AI) systems in the field of colorectal endoscopy is currently booming, colorectal cancer being, by its frequency and severity, a real public health problem. In terms of image analysis, AI is indeed able to perform many tasks simultaneously (lesion detection, classification, and segmentation) and to combine them. Lesion detection is thus the starting point of the whole chain to choose at the end the most appropriate treatment for the patient. Large-scale studies have demonstrated the superiority of artificial intelligence-assisted detection over the usual detection by gastroenterologists, mainly for the detection of sub-centimeter polyps. However, the investigators have shown that a recent computer-aided detection system (CADe) such as the ENDO-AID software in combination with the EVIS X1 video column (Olympus, Tokyo, Japan) may present difficulties in the detection of flat lesions such as sessile serrated lesions (SSLs) and non-granular laterally spreading tumors (LST-NGs). This represents a major challenge because in addition to their shape being difficult to identify for the human eye in practice and where AI assistance would be of great value, these rare lesions are associated with advanced histology. In addition, the investigators recently described the case of a worrisome false negative of AI-assisted colonoscopy, which failed to detect a flat adenocarcinoma in the transverse colon. Therefore, it is important to measure the false negative rate of AI detection based on the macroscopic shape of the lesion. Comparing this rate to the human endoscopist’s false negatives would improve the performance of AI for this specific lesion subtype in the future.

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Multidimensional raw files used for MZA benchmarking

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Identification of relationships between Molecular and Imaging Phenotypes in Non-small cell lung cancer using radiogenomics Map

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2022-04-27 | PXD023904 | Pride

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2024-09-12 | GSE254841 | GEO

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