Software Defined Instrument (AI based Software as Medical Device), CLARA case study
By: Vance Chang
Tags: Clara, Medical Device, Software Defined I, AI, Nvidia

More AI based platforms are available for medical device development. Here is an illustration how to use such platform. Also, the product differentiation will be depended on the “content”.

Original post released on Oct. 20, 2020, at Linkedin.

According to Bio Taiwan committee conference on 9/1, this topic is introduced by Eunice Chiu of Nvidia shown as attached.

Cited from lecturer, any form factor is described for software defined instrument. My preliminary understanding may be analogous to Labview by NI (National Instruments).

Both Nvidia and NI can be considered as platform to integrate instruments as operational network. Nvidia integrates AI, IOMT (internet of medical thing), and edge in the platform basis. The scale is significantly different.

I was triggered with the concept, and conduct a walk-through analysis to see how to implant the idea and what can be explored? It is just for personal leisure.

Step 1. Understanding the platform

I went to the Nvidia’s page and try to under the core of platform, which is Clara as shown in the following.

https://developer.nvidia.com/clara

I choose Clara for Medical Imaging as my research top, and the following diagram illustrate the application flow clearly.

Step 2 Pick up my interest

First of all, I get rid of the embedded application as following.

It is due to the thought as below.

(1) I can not develop a universal embedded application for any brand’s imaging device since each system employ different framework, and it makes regulatory tasks extremely complicated with risk management and system validation. And it is just for one model.

(2) It is highly preferred to make front-end sensing instrument simple and reliable. Maybe some pre-processing tasks can be done in the front end, still, leaving complicated tasks to high power computing end.

Based on the above concept, I will prefer to develop software package in the high power computing end like cloud or dedicated GPU computer. To be precise, it will be a software module integrated to #PACS/RIS system to provide radiologist the aide of clinical assessment.

Step 3 Intended Use

By benchmarking the market available product, HealthPNX of Zebra Medical Vision is the one close to product of my interest. The 510K can be referred as the following link.

https://www.accessdata.fda.gov/cdrh_docs/pdf19/K190362.pdf

The intended use /Indication for use is shown as following.

The product code is QFM in FDA with following information.

https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpcd/classification.cfm?id=5548

Step 4 Development

For those company familiar with FDA regulation, the first step is to follow FDA guideline, “Guidance for the Content of Premarket Submissions for Software Contained in Medical Device”.

https://www.fda.gov/media/73065/download

It is an “harmonized” standard for both FDA and IEC-62304 for CE. The idea is similar to the principle of design control for medical device by FDA. Risk analysis must be applied, and traceability analysis is needed.

Next, this AI application is based on the foundation of Clara, and then we need to check if definition of Off The Shelf (OTS) software is applicable. In fact, I am not familiar with the structure of Clara, and suggest every developer should check the following.

https://www.fda.gov/media/71794/download

Since FDA had started discussion paper regarding AI and Machine learning based software as medical device, it will be better to check what in FDA’s mind.

https://www.fda.gov/media/122535/download

There is a description of Total Product Life Cycle (TPLC) illustrated by FDA as below.

It is the extension of “Guidance for the Content of Premarket Submissions for Software Contained in Medical Device” in consideration of the nature of AI or Machine learning. According to FDA, This proposed regulatory approach would apply to only those AI/ML basedSaMD that require premarket submission.

Step 5: Submission for clearance or certificate and another life cycle

The previous step are focused with FDA clearance. However, for CE certificate, IEC-62304 must be followed, and above-mentioned “harmonized” standard for most firmware based medical device will not be sufficient. In IEC-62304, configuration management of software are highly emphasized. Thus, the developer must follow up the Clara framework quite carefully, and track down any update/upgrade of framework. As shown in TPLC approach, data re-training will be continuous work , and it hints importance of software configuration management.

Observations:

After this walk-through of software product development, there are some interesting findings.

(1) Clara appears to be a powerful tool. But it is accessible to everyone, and how does my work differentiate to others? It is believed that the annotation contents will make the difference. Thus, such platform may favor “content provider” most.

(2) Maybe there will be one more step for Clara as following.

https://www.fda.gov/media/119722/download

It is Software Pre-Cert Program and TPLC approach applied. It may be another corner stone for similar AI/Machine learning platform in medical application.

Note(10/24/2020): If NVIDIA’s goal is to provide a development platform for AI medical imaging, just like Microsoft’s Visual Studio Framework. In fact, the major difference will be regulatory issue like 510K since the end product is considered as medical device. In fact, such platform idea for medical device had been promoted by MCU supplier like TI. For example, application design and schematic had been provided. It is not quite a success, and regulatory issue is one of the main reason.

In this case, it is believed that this model will work better if the followings are provided. (1) A reference design with 510K clearance is available, which can be served for the purpose of substantially equivalence. (2) Then, based on above design, an “open source” regulatory reference package including IEC 62304 documents, ISO 14971 risk management report, pivot performance study report, and etc.

If Nvidia can be considered as the platform such as Netflix, then the developer of this platform will be content provider, for instance producer of Queen’s Gambit.

Thus, developers can focus the effort in product differentiation by introducing proprietary learning models, which will be the content of this platform. It may facilitate the fabrication of an ecosystem for medical imaging solution.