Recent AI Solutions
Helping organizations overcome challenges to deploy & scale AI
SFL Scientific Powering CBS Sports Analytics
From next generation stats and fantasy to play-by-play insights, analyzing millions of data points helps dissect the game matchups and enhances the fan experience. ViacomCBS leveraged the expertise of SFL Scientific to create in-game probability models, utilizing real-time data analysis and machine learning to create custom win and play by play analytics for each major sport.
Capturing data across traditional box scores, play-by-play, historical statistics and game conditions, and utilizing 100s of new metrics, SFL Scientific built machine learning solutions giving CBS unique capabilities for real-time score prediction, stats, and win probability predictions. New models give fans the most up to date Real-Time Win Probability across the CBS Sports ecosystem.
AI-Based Diagnostic Tools for Medical Imaging
Our work with InformAI focused on developing a standalone system to detect 23 conditions in the paranasal sinuses for ENT & radiology applications at human accuracy for FDA approval.
The radiology tool displays the indication, location, & a prediction heatmap, processing 3D CTs images, acting as an end-to-end diagnostic pipeline that ingests real-time scans and is currently being utilized by radiologists in the Texas Medical Center.
AI Inspection & Defect Detection Using Drones
Our work with a national power organization included developing and deploying a multi-label classifier to detect various defects such as corrosion, cracks, honeycombing, and other defects in large industrial structures.
The system is capable of recognizing desired features in real-time drone images and video, allowing inspection to occur at the edge or saved for later batch processing. The deep learning-based system has segmentation accuracy in the mid to high 90s.
Natural Language Processing for Petabyte-Scale Data Monitoring
Our work as a part of a larger US Army effort focused on cutting edge NLP solutions leveraging BERT and custom language models to develop a question and answer system for situational awareness and intelligence.
The deep learning powered search engine finds top relevant passages from billions of multilingual documents and allows for entity extraction, topic modeling, location analytics, and other key features. The solution is designed to work in any environment, on-premise, via AWS, or local services, and scalable to output results in seconds.
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Solving AI R&D Challenges
Automated Clinical Data Review using Graph Databases
Working with a global pharmaceutical contract research & trial management organization, SFL Scientific developed an automated data review system combining network analysis anomaly detection and AI data review. The solution allows of richer representations of relational and traditional non-connected clinical, patient, and temporal information, allowing for automated data review and holistic examination of outlier and atypical medical patterns.
Detection Ability: The solution is built to support both traditional on-prem graph (Neo4J, Orient, etc.) and cloud-based services (AWS Neptune) and allows trial managers to monitor and examine the potentially thousands of exceptions such as adverse events to reduce manual review queries. With latent vector and graph analysis coupled with ML for data review, the novel capability provides clustering and insights into patient ‘communities’ by custom data variables (AE, Lab, Demo, History, etc.).