Jiang et al., 2023 - Google Patents
The use of voting ensembles to improve the accuracy of deep neural networks as a non-invasive method to predict embryo ploidy statusJiang et al., 2023
View HTML- Document ID
- 12748935912533977196
- Author
- Jiang V
- Kandula H
- Thirumalaraju P
- Kanakasabapathy M
- Cherouveim P
- Souter I
- Dimitriadis I
- Bormann C
- Shafiee H
- Publication year
- Publication venue
- Journal of Assisted Reproduction and Genetics
External Links
Snippet
Purpose To determine if creating voting ensembles combining convolutional neural networks (CNN), support vector machine (SVM), and multi-layer neural networks (NN) alongside clinical parameters improves the accuracy of artificial intelligence (AI) as a non …
- 230000001537 neural 0 title abstract description 17
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
- G06F19/34—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
- 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
- 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
- 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
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
- G06Q10/063—Operations research or analysis
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
-
- 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
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Liao et al. | Development of deep learning algorithms for predicting blastocyst formation and quality by time-lapse monitoring | |
Bormann et al. | Performance of a deep learning based neural network in the selection of human blastocysts for implantation | |
Berntsen et al. | Robust and generalizable embryo selection based on artificial intelligence and time-lapse image sequences | |
Fernandez et al. | Artificial intelligence in the IVF laboratory: overview through the application of different types of algorithms for the classification of reproductive data | |
Huang et al. | An artificial intelligence model (euploid prediction algorithm) can predict embryo ploidy status based on time-lapse data | |
Barnes et al. | A non-invasive artificial intelligence approach for the prediction of human blastocyst ploidy: a retrospective model development and validation study | |
Swain et al. | AI in the treatment of fertility: key considerations | |
Jiang et al. | The use of voting ensembles to improve the accuracy of deep neural networks as a non-invasive method to predict embryo ploidy status | |
Leahy et al. | Automated measurements of key morphological features of human embryos for IVF | |
Glatstein et al. | New frontiers in embryo selection | |
Kan-Tor et al. | Automated evaluation of human embryo blastulation and implantation potential using deep‐learning | |
Hanassab et al. | The prospect of artificial intelligence to personalize assisted reproductive technology | |
Letterie | Three ways of knowing: the integration of clinical expertise, evidence-based medicine, and artificial intelligence in assisted reproductive technologies | |
Jiang et al. | Non-invasive genetic screening: current advances in artificial intelligence for embryo ploidy prediction | |
Curchoe | All models are wrong, but some are useful | |
Jiang et al. | The role of artificial intelligence and machine learning in assisted reproductive technologies | |
Fjeldstad et al. | An artificial intelligence tool predicts blastocyst development from static images of fresh mature oocytes | |
Pavlovic et al. | Current applications of artificial intelligence in assisted reproductive technologies through the perspective of a patient's journey | |
Zabari et al. | Delineating the heterogeneity of embryo preimplantation development using automated and accurate morphokinetic annotation | |
Marsh et al. | A proof of concept for a deep learning system that can aid embryologists in predicting blastocyst survival after thaw | |
Haugen et al. | Sperm motility assessed by deep convolutional neural networks into WHO categories | |
Campbell et al. | In vitro fertilization and andrology laboratory in 2030: expert visions | |
Yang et al. | BlastAssist: a deep learning pipeline to measure interpretable features of human embryos | |
Yuan et al. | Application of artificial neural networks in reproductive medicine | |
Berman et al. | Deep learning for embryo evaluation using time-lapse: a systematic review of diagnostic test accuracy |