Zou et al., 2021 - Google Patents
Automated measurements of body composition in abdominal CT scans using artificial intelligence can predict mortality in patients with cirrhosisZou et al., 2021
View PDF- Document ID
- 8082404539521914112
- Author
- Zou W
- Enchakalody B
- Zhang P
- Shah N
- Saini S
- Wang N
- Wang S
- Su G
- Publication year
- Publication venue
- Hepatology communications
External Links
Snippet
Body composition measures derived from already available electronic medical records (computed tomography [CT] scans) can have significant value, but automation of measurements is needed for clinical implementation. We sought to use artificial intelligence …
- 201000004044 liver cirrhosis 0 title abstract description 35
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0012—Biomedical image inspection
- G06T7/0014—Biomedical image inspection using an image reference approach
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
- G06F19/345—Medical expert systems, neural networks or other automated diagnosis
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by the preceding groups
- G01N33/48—Investigating or analysing materials by specific methods not covered by the preceding groups biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/34—Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
- G06F19/3431—Calculating a health index for the patient, e.g. for risk assessment
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/32—Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
- G06F19/36—Computer-assisted acquisition of medical data, e.g. computerised clinical trials or questionnaires
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/10—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
- G06F19/24—Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology for machine learning, data mining or biostatistics, e.g. pattern finding, knowledge discovery, rule extraction, correlation, clustering or classification
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20212—Image combination
- G06T2207/20224—Image subtraction
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20092—Interactive image processing based on input by user
- G06T2207/20104—Interactive definition of region of interest [ROI]
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2800/00—Detection or diagnosis of diseases
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N23/00—Investigating or analysing materials by the use of wave or particle radiation not covered by G01N21/00 or G01N22/00, e.g. X-rays or neutrons
- G01N23/02—Investigating or analysing materials by the use of wave or particle radiation not covered by G01N21/00 or G01N22/00, e.g. X-rays or neutrons by transmitting the radiation through the material
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2223/00—Investigating materials by wave or particle radiation
- G01N2223/60—Specific applications or type of materials
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Zou et al. | Automated measurements of body composition in abdominal CT scans using artificial intelligence can predict mortality in patients with cirrhosis | |
Keidar et al. | COVID-19 classification of X-ray images using deep neural networks | |
Fang et al. | Radiomics nomogram for the prediction of 2019 novel coronavirus pneumonia caused by SARS-CoV-2 | |
Greco et al. | Artificial intelligence and abdominal adipose tissue analysis: a literature review | |
Kollias et al. | Ai-enabled analysis of 3-d ct scans for diagnosis of covid-19 & its severity | |
Brugnara et al. | Automated volumetric assessment with artificial neural networks might enable a more accurate assessment of disease burden in patients with multiple sclerosis | |
Gao et al. | Chest X-ray image analysis and classification for COVID-19 pneumonia detection using Deep CNN | |
Thong et al. | Diagnostic test accuracy of artificial intelligence-based imaging for lung cancer screening: A systematic review and meta-analysis | |
Wang et al. | Automated measurements of muscle mass using deep learning can predict clinical outcomes in patients with liver disease | |
Weikert et al. | Prediction of patient management in COVID-19 using deep learning-based fully automated extraction of cardiothoracic CT metrics and laboratory findings | |
Mader et al. | Quantification of COVID-19 opacities on chest CT–evaluation of a fully automatic AI-approach to noninvasively differentiate critical versus noncritical patients | |
Esposito et al. | Artificial intelligence in predicting clinical outcome in COVID-19 patients from clinical, biochemical and a qualitative chest X-ray scoring system | |
Marasco et al. | Imaging Software‐Based Sarcopenia Assessment in Gastroenterology: Evolution and Clinical Meaning | |
Sachan et al. | Artificial intelligence for discrimination of Crohn's disease and gastrointestinal tuberculosis: A systematic review | |
Roblot et al. | Validation of a deep learning segmentation algorithm to quantify the skeletal muscle index and sarcopenia in metastatic renal carcinoma | |
Chen et al. | Establish a new diagnosis of sarcopenia based on extracted radiomic features to predict prognosis of patients with gastric cancer | |
Huang et al. | Predicting malnutrition in gastric cancer patients using computed tomography (CT) deep learning features and clinical data | |
Yoo et al. | Generative adversarial network for automatic quantification of Coronavirus disease 2019 pneumonia on chest radiographs | |
Taheriyan et al. | Prediction of COVID-19 patients’ Survival by deep learning approaches | |
Shiraishi et al. | Analysis of bone erosions in rheumatoid arthritis using HR-pQCT: Development of a measurement algorithm and assessment of longitudinal changes | |
Schniering et al. | Resolving phenotypic and prognostic differences in interstitial lung disease related to systemic sclerosis by computed tomography-based radiomics | |
US20230252633A1 (en) | Method for biomarker estimation | |
Tricarico et al. | Deep regression by feature regularization for COVID-19 severity prediction | |
Whiting et al. | Updating QUADAS: Evidence to inform the development of QUADAS-2 | |
Baral et al. | Redefining lobe-wise ground-glass opacity in COVID-19 through deep learning and its correlation with biochemical parameters |