Anatomy Data Cohorting for Automated Attribute Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Medical professionals face challenges in detecting medically relevant attributes from large quantities of unlabeled clinical data, which can obscure early detection of conditions like atrial fibrillation.
Innovation Solution
An apparatus and method for attribute detection in anatomy data, utilizing a processor and memory to receive and process reference anatomy data and metadata, extract anatomic features, group data into cohorts, label query data, and detect attributes based on these labels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If medical professionals manually analyze large quantities of unlabeled clinical data to detect medically relevant attributes, then detection accuracy may be maintained, but time consumption and workload increase significantly
Solution Approach 1:
The system enables self-service by automatically detecting medically relevant attributes through machine learning models that process clinical data without requiring manual analysis. The apparatus autonomously performs feature extraction, cohort grouping, and attribute detection, allowing the diagnostic system to serve itself rather than relying on continuous human intervention for each analysis task.
Solution Approach 2:
The patent replaces the mechanical system of manual data analysis with an automated computational system. Machine learning models and algorithms substitute human professionals' manual examination processes, transforming the mechanical act of reviewing clinical data into an automated information processing system that extracts features, groups cohorts, and detects attributes computationally.
2Measurement precision
If manual analysis methods are used to process clinical data, then detailed examination is possible, but productivity and efficiency decrease
Solution Approach 1:
The diagnostic system performs self-service by automatically executing the complete analysis pipeline from raw clinical data to detected medical attributes. The machine learning models independently conduct feature extraction, cohort grouping, and attribute detection without requiring manual intervention at each stage, thereby maintaining examination detail while dramatically improving diagnostic efficiency and throughput.
Solution Approach 2:
The patent substitutes manual examination mechanics with automated computational processing. The system replaces human professionals' detailed review process with machine learning algorithms that perform feature extraction, cohort grouping, and attribute detection computationally, achieving both detailed examination and high productivity simultaneously through automated information processing.
3Reliability
If repetitive diagnostic procedures are performed manually, then thorough evaluation is achieved, but resource consumption and operational complexity increase
Solution Approach 1:
The patent merges multiple repetitive diagnostic procedures into a single integrated automated system. The machine learning models combine feature extraction, cohort grouping, and attribute detection into one unified processing pipeline, eliminating the need for separate manual procedures while maintaining evaluation thoroughness and reducing operational complexity.
Solution Approach 2:
The system achieves self-service by automatically performing thorough evaluation through integrated machine learning models. The apparatus independently executes the complete diagnostic workflow from data processing to attribute detection without requiring coordinated manual procedures, thereby maintaining reliability through thorough evaluation while reducing operational complexity.
Data Source
AI summary
Apparatus for attribute detection in anatomical data and methods used therein are described, wherein the apparatus includes a processor and a memory communicatively connected to the processor, wherein the memory includes instructions configuring the processor to receive reference anatomy data and reference metadata, extract anatomic features from the received reference anatomy data and reference metadata, group the received reference anatomy data and reference metadata into a plurality of cohorts with one or more similar groups of anatomic features as a function of the extracted anatomic features, receive query anatomy data and query metadata, label the received query anatomy data and query metadata as a function of the plurality of cohorts, and detect at least an attribute as a function of the label.