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EnergyTech
Maximizing energy output using renewables equipment
ShipTech
Optimizing vessels' engines for greener maritime
HealthTech
Enabling remote diagnostics using data from wearables
Life Sciences
Utilizing AI across the medical treatment value chain

How the iPlena Сompany Utilized AI in Physiotherapy

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Client Background

iPlena is a mobile body scanning AI for tailored osteopathic training that rewires the central nervous system. iPlena aims to provide personalized training that takes just 3 minutes a day and is faster than a morning cup of coffee. The company’s AI analyzes self-taken pictures to give users the perfect recommendation for a healthier posture.

Customer Challenge

iPlena needed to build a mobile application with an AI/ML component that could recommend a set of therapeutic exercises to compensate for the user’s physiological problems. They also needed to develop AI/ML components with computer vision that could analyze human body posture and propose treatments and exercises.

Solution

Neurons Lab, a cloud network of scientists, engineers, and developers passionate about revolutionizing businesses’ futures with AI and machine learning technologies, partnered with iPlena to create a secure and scalable foundation for AWS. This included AWS Control Tower, AWS Security Hub, AWS CloudTrail, AWS Config, Amazon GuardDuty, Amazon VPC, AWS Service Catalog, and other AWS services.

Neurons Lab also created MLOps pipelines to ensure accessible building, training, and deployment processes for AI/ML models using Amazon SageMaker Pipelines, Amazon SageMaker Training and Inferences, AWS CodePipeline, AWS CodeBuild, and more. They also created a self-service portal for infrastructure patterns using AWS Service Catalog, which included MLOps pipelines, VPC products, and the AWS Account Factory.

AI ML model identifies key points in the human body

A key point detector AI/ML model was built, trained, and deployed using Amazon SageMaker to identify key points in the human body in photos and videos. An anomaly diagnostic AI/ML model was also built, trained, and deployed using Amazon SageMaker to detect anomalies in human posture.

iPlena IOS Mobile Application

An iOS mobile application was created that recommends a set of therapeutic exercises aimed at compensating for the physiological problems of the user based on photos and videos analyzed through the mobile application. The app was deployed on Amazon EKS, Amazon RDS, Amazon ElastiCache, and Amazon DynamoDB.

AWS solution architecture

Results and Benefits

  • Increased deployment frequency, reduced lead times, and decreased failure rates resulting in improved efficiency
  • Environment met security and compliance requirements ensuring patient safety and privacy
  • Self-diagnosis and therapeutic exercises provided through the mobile application resulted in patients taking control over their own health and being able to continue treatment outside of the clinic
  • Efficient and accurate diagnoses using AI technology resulted in reduced appointment times and increased capacity for patients, leading to increased revenue for the clinic
  • Patients were able to get recommendations for a set of therapeutic exercises aimed at compensating for their physiological problems without visiting a physiotherapist, improving the overall patient experience.

iPlena vs Other apps

About the Neurons Lab

Neurons Lab is a team of experts in AI and machine learning technologies. Their goal is to help businesses achieve a strategic advantage by creating tailor-made solutions to enhance performance and social responsibility. They also provide opportunities for talented individuals to work independently while taking on important challenges.

Some of our most significant focus is on the HealthTech and EnergyTech industries. Through HealthTech, the team aims to improve human lives, well-being, and longevity. In EnergyTech, they support industrial companies in becoming more efficient and reducing environmental harm through energy conservation and emission reduction.


Have an idea for using AI/ML in the healthcare domain?

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