Modernizing wound care with Spectral MD, powered by Amazon SageMaker

Spectral MD, Inc. is a clinical research stage medical device company that describes itself as “breaking the barriers of light to see deep inside the body.” Recently designated by the FDA as a “Breakthrough Device,” Spectral MD provides an impressive solution to wound care using cutting edge multispectral imaging and deep learning technologies. This Dallas-based company relies on AWS services including Amazon SageMaker and Amazon Elastic Compute Cloud (Amazon EC2) to support their unprecedented wound care analysis efforts. With AWS as their cloud provider, the Spectral MD team can focus on healthcare breakthroughs, knowing their data is stored and processed swiftly and effectively.

“We chose AWS because it gives us access to the computational resources we need to rapidly train, optimize, and validate the state-of-the-art deep learning algorithms used in our medical device,” explained Kevin Plant, the software team lead at Spectral MD. “AWS also serves as a secure repository for our clinical dataset that is critical for the research, development and deployment of the algorithm.”

The algorithm is the 10-year-old company’s proprietary DeepView Wound Imaging System, which uses a non-invasive digital approach that allows clinical investigators to see hidden ailments without ever coming in contact with the patient. Specifically, the technology combines visual inputs with digital analyses to understand complex wound conditions and predict a wound’s healing potential. The portable imaging device in combination with computational power from AWS, allows clinicians to capture a precise snapshot of what is hidden to the human eye.

Spectral MD’s revolutionary solution is possible thanks to a number of AWS services for both core computational power and machine learning finesse. The company stores data captured by their device on Amazon Simple Storage Service (Amazon S3), with the metadata living in Amazon DynamoDB. From there, they back up all the data in Amazon S3 Glacier. This data fuels their innovation with AWS machine learning (ML).

To manage the training and deployment of their image classification algorithms, Spectral MD uses Amazon SageMaker and Amazon EC2. These services also help the team to achieve improved algorithm performance and to conduct deep learning algorithm research.

Spectral MD particularly appreciates that using AWS services saves the data science team a tremendous amount of time. Plant described, “The availability of AWS on-demand computational resources for deep learning algorithm training and validation has reduced the time it takes to iterate algorithm development by 80%. Instead of needing weeks for full validation, we’re now able to cut the time to 2 days. AWS has enabled us to maximize our algorithm performance by rapidly incorporating the latest developments in the state-of-the-art of deep learning into our algorithm.”

That faster timeline in turn benefits the end patients, for whom time is of the essence. Diagnosing burns quickly and accurately is critical for accelerating recovery and can have important long-term implications for the patient. Yet, current medical practice (without Spectral MD) suffers a 30% diagnostic error rate, meaning that some patients unnecessarily treated with surgery while others who would have benefited from surgery are not offered that option.

Spectral MD’s solution takes advantage of the natural patterns in the chemicals and tissues that compose human skin – and the natural pattern-matching abilities of ML. Their model has been trained on thousands of images of accurately diagnosed burns. Now, it is precise enough that the company can create datasets from scratch that differentiate pathologies from healthy skin, operating to a degree that is impossible for the human eye.

These datasets are labeled by expert clinicians using Amazon SageMaker Ground Truth. Spectral MD has extended Amazon SageMaker Ground Truth with the ability to review clinical reference data stored in Amazon S3. During the labeling process this provides clinicians with the ideal information set to maximize the accuracy of the diagnostic ground truth labels.

Going forward, Spectral MD plans to push the boundaries of ML and of healthcare. Their team has recently been investigating the use of Amazon SageMaker Neo for deploying deep learning algorithms to edge hardware. In Plant’s words, “There are many barriers to incorporating new technology into medical devices. But AWS continually improves how easy it is for us to take advantage of new and powerful features; no one else can keep pace with AWS.”

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About the Author

Marisa Messina is on the AWS ML marketing team, where her job includes identifying the most innovative AWS-using customers and showcasing their inspiring stories. Prior to AWS, she worked on consumer-facing hardware and then university-facing cloud offerings at Microsoft. Outside of work, she enjoys exploring the Pacific Northwest hiking trails, cooking without recipes, and dancing in the rain.

 

 

 

 

 

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