Abo-Zahhad et al., 2024 - Google Patents
Minimization of occurrence of retained surgical items using machine learning and deep learning techniques: a reviewAbo-Zahhad et al., 2024
View HTML- Document ID
- 10205207619180302220
- Author
- Abo-Zahhad M
- El-Malek A
- Sayed M
- Gitau S
- Publication year
- Publication venue
- BioData Mining
External Links
Snippet
Retained surgical items (RSIs) pose significant risks to patients and healthcare professionals, prompting extensive efforts to reduce their incidence. RSIs are objects inadvertently left within patients' bodies after surgery, which can lead to severe …
- 238000000034 method 0 title abstract description 85
Classifications
-
- 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
- G06F19/321—Management of medical image data, e.g. communication or archiving systems such as picture archiving and communication systems [PACS] or related medical protocols such as digital imaging and communications in medicine protocol [DICOM]; Editing of medical image data, e.g. adding diagnosis information
-
- 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
- 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
- G06F19/322—Management of patient personal data, e.g. patient records, conversion of records or privacy aspects
-
- 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
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/22—Health care, e.g. hospitals; Social work
- G06Q50/24—Patient record management
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Pinto-Coelho | How artificial intelligence is shaping medical imaging technology: A survey of innovations and applications | |
Park et al. | Deep learning–assisted diagnosis of cerebral aneurysms using the HeadXNet model | |
Kennedy-Metz et al. | Computer vision in the operating room: Opportunities and caveats | |
Elyan et al. | Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward. | |
Chen et al. | Synthetic data in machine learning for medicine and healthcare | |
Esteva et al. | Deep learning-enabled medical computer vision | |
Dunnmon et al. | Assessment of convolutional neural networks for automated classification of chest radiographs | |
Chadebecq et al. | Computer vision in the surgical operating room | |
Padoy | Machine and deep learning for workflow recognition during surgery | |
Habuza et al. | AI applications in robotics, diagnostic image analysis and precision medicine: Current limitations, future trends, guidelines on CAD systems for medicine | |
Lakhani et al. | Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks | |
US9401020B1 (en) | Multi-modality vertebra recognition | |
Galić et al. | Machine learning empowering personalized medicine: A comprehensive review of medical image analysis methods | |
Hendrix et al. | Development and validation of a convolutional neural network for automated detection of scaphoid fractures on conventional radiographs | |
Van Assen et al. | Beyond the artificial intelligence hype: what lies behind the algorithms and what we can achieve | |
Mall et al. | Modeling visual search behavior of breast radiologists using a deep convolution neural network | |
Gupta et al. | Artificial intelligence: a new tool in surgeon's hand | |
Ren et al. | Deep learning detection of subtle fractures using staged algorithms to mimic radiologist search pattern | |
Katzmann et al. | Explaining clinical decision support systems in medical imaging using cycle-consistent activation maximization | |
Gong et al. | Using deep learning to identify the recurrent laryngeal nerve during thyroidectomy | |
Filice et al. | Effectiveness of deep learning algorithms to determine laterality in radiographs | |
Roth et al. | Multispecialty enterprise imaging workgroup consensus on interactive multimedia reporting current state and road to the future: HIMSS-SIIM collaborative white paper | |
Binol et al. | OtoXNet—Automated identification of eardrum diseases from otoscope videos: A deep learning study for video-representing images | |
Prabha et al. | A big wave of deep learning in medical imaging-analysis of theory and applications | |
Li et al. | Role of Artificial Intelligence in Medical Image Analysis: A Review of Current Trends and Future Directions |