Impact AI

In this episode, I talk with John Bertrand, CEO of Digital Diagnostics, about autonomous diagnostics. Digital Diagnostics transforms the quality, accessibility, equity, and affordability of healthcare with AI-powered diagnostics. They developed the first FDA-cleared autonomous AI system.Quotes:“So we look for diagnostics where there's an established understanding of what the disease is and there's a gold standard as to how to measure that.”“We'll naturally start with an area where positive and negative is a very binary decision that is almost mathematically derived.”“It goes back to picking the right types of disease states to make sure that the gold standard already exists.”“How do you take images that have different coverage of the retina but make sure that you piece them together in a way that the processing part of the system is getting a consistent image that they're looking at every single time so that the algorithm remains consistent and we don't have to have different algorithms per vendor that we're interacting with.”“We’re pretty proud of the fact we’ve been able to do that first kind of assistive feedback for the provider.”“We want every single patient, regardless of their background, to receive consistent quality of diagnostic output. What that means is that we actually have to build our training data sets as well as our clinical validation studies and trials to take into account a diverse population set.”“Continuous learning versus locked algorithms is another key factor. . . Would you really want that algorithm to adjust to the most recent data it's seeing, thinking it's attempting to become more accurate, when in fact it's really more optimizing for the ethnicity of the folks in that particular region, the sun rises on the east coast to the United States, everybody further east goes to bed. Now the algorithm’s been indexed towards another group from a ethnicity perspective, that’s no longer representative of where the testing’s being done as the sun rises in New York.”“How do we ensure that we create confidence with regulators, with providers, and with patients that we've actually thought through this?”“We can literally break down for you what the computer saw, why graded it out what it did, and why it gave you the results it did.”“Your algorithm should be explainable so people trust the technology, understand how it works.”“Also explainability helps you drive better accuracy and that you understand why you're getting the result that you're getting with the black box approach.”“You really want to work within the healthcare system when you’re building these types of businesses.”“If you're going to chart that course and really carry through to fruition, your vision of building an algorithm that impacts patient lives, I think you really need to center the culture of the business around a commonly shared vision for the mission of what you're trying to do.”Links:Digital DiagnosticsResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

In this episode, I talk with David Schurman, co-founder and CTO of Perennial, about their verification platform for climate-smart agriculture. Perennial uses geospatial data and machine learning to unlock agricultural soils as the world’s largest carbon sink.Highlights:How Perennial gathers and annotates training data from satellites and ground-based observations.Handling variations across satellites and geographic locations.Stratifying training data across the kinds of variables that matter.Collaboration between machine learning engineers, remote sensing scientists, and crop scientists.The importance of gathering more training data than you think you’ll need.Respecting the data.The nuance of communicating performance metrics.Links:Perennial’s websitePerennial on LinkedInDavid Schurman on LinkedInResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

In this episode, I talk with Matt Alderdice, Head of Data Science at Sonrai Analytics, about precision medicine. Sonrai Analytics automates laborious data processes and speeds up new drug and healthcare developments.Highlights:Machine learning for automating time-consuming and tedious analysis of microscopy images.Training for machine learning practitioners new to pathology by integrating domain experts with your team.Involving stakeholders throughout a project.Literature reviews to search for associated publications and potential solutions to avoid overly complicated solutions.Validating models with ethnically diverse datasets.Analytical validation for differing stains, scanners, and operators.Clinical validation on a held out dataset in the same environment as would be in the clinic.Identifying relevant metrics from conversations with pathologists, oncologists, nurses, and patients.Focus on the problem you’re trying to solve – AI is just a tool.Links:Sonrai Analytics’ websiteMatt Alderdice on LinkedInResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

In this episode, I talk with David Golan, co-founder and CTO of Viz.ai, about diagnosis of acute and emergent diseases. Viz.ai increases the speed of diagnosis and care for a variety of conditions to improve the lives of patients.Highlights:Increasing access to lifesaving treatments.The importance of the full system, not just the machine learning component, in accelerating workflows.Their clinical AI team includes med students, MDs, biomedical engineers, and neuropsychologists.Bias can be created by a lower performance on a subset of the population in a way that is unknown to developers, users, and clinicians.Careful monitoring of algorithms to identify subsets of data with poor performance.Unbiased collection and stratification of data for FDA submission.The importance of good annotation and monitoring infrastructure.Relatively simple model architectures can take you a long way.Links:Viz.ai’s websiteDavid Golan on LinkedInResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

In this episode, I talk with Steve Brumby, co-founder, CEO and CTO of Impact Observatory, about sustainability and environmental risk analysis. Impact Observatory uses satellite imagery and machine learning to empower decision-makers with planetary insights.Highlights:Using machine learning to generate thematic maps to represent land cover and land use.Geospatial data from the European Space Agency’s Copernicus program that is available on a variety of platforms.The importance of identifying the relevant output for end users and others in the value chain.How machine learning engineers sometimes discover things used by remote sensing scientists that are no longer necessary.Keeping models simple.Mitigating bias in models by using large and globally diverse datasets.Get to know your customer and their pain points, then craft a machine learning solution that works for them – if you’re lucky, it’ll also work for others.Finding the things you’re passionate about – both the technology and helping the customers in that space.Links:Impact Observatory's websiteSteve Brumby on LinkedInK. Karra, C. Kontgis, Z. Statman-Weil, J. C. Mazzariello, M. Mathis and S. P. Brumby, "Global land use / land cover with Sentinel 2 and deep learning," 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, pp. 4704-4707, doi: 10.1109/IGARSS47720.2021.9553499Brown, C.F., Brumby, S.P., Guzder-Williams, B. et al. Dynamic World, Near real-time global 10 m land use land cover mapping. Scientific Data 9, 251 (2022). https://doi.org/10.1038/s41597-022-01307-4Impact Observatory’s datahttps://www.arcgis.com/home/item.html?id=d6642f8a4f6d4685a24ae2dc0c73d4achttps://planetarycomputer.microsoft.com/dataset/io-lulc-9-classUN Biodiversity Lab: country dashboard for AustraliaResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

In this episode, I talk with Dean Freestone, co-founder and CEO of Seer, about epilepsy. Seer uses home monitoring to diagnose and manage neurological conditions, relieving bottlenecks in the healthcare system.Highlights:Using machine learning to summarize data to reduce the labor intensive search for episodic events.Handling imbalanced datasets. Controlling the workflows to enable gathering and annotating huge datasets.Working with technicians to speed up review of EEG data.Using machine learning to capture features that doctors can’t describe.Dealing with low inter-reviewer agreement from clinicians.How bias can manifest is neurological data.Do not underestimate the cost and amount of work to build a healthcare AI startup.Links:Seer’s websiteSeer on LinkedInDean Freestone on LinkedInResources for Computer Vision Teams:LinkedIn – Connect with Heather.Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.Computer Vision Advisory Services – Monthly advisory services to help you strategically plan your CV/ML capabilities, reduce the trial-and-error of model development, and get to market faster.

Welcome to Impact AI, the podcast for startups who want to create a better future through the use of machine learning.I'm your host, Heather Couture.In this podcast, you’ll learn how to build a mission-driven machine learning company. I’ll be interviewing innovators and entrepreneurs from a variety of industries: healthcare, drug development, environmental, agriculture, and many more.Each is striving to solve a problem that they are passionate about. They will talk about the role machine learning plays in their technology and the impact of their product.They will also help me uncover machine learning challenges like data annotation, generalizability, explainability, bias, and collaboration across disciplines – and best practices for tackling them in a startup environment.Now, who am I?I’m a consultant with almost 2 decades of experience in computer vision and machine learning for a variety of applications. From manufacturing to planetary science to commercial media to cancer research.I completed a Masters at Carnegie Mellon University and a PhD in Computer Science at the University of North Carolina. As a researcher, I published in top-tier computer vision and medical imaging venues. Now I write regularly on LinkedIn, for my newsletter Pathology ML Insights, and for a variety of trade publications.I offer consulting services through my company Pixel Scientia Labs to help startups get to market faster by building more generalizable computer vision models. I make use of the latest machine learning research to amplify their results and support their in-house team for the long term. My mission is to fight cancer and climate change with AI – and I do that by strengthening the machine learning component of my clients’ most impactful projects.My hope for this podcast is to share machine learning best practices more widely so that many others can benefit as they work towards solving important problems.Thanks for listening.Please hit subscribe to be notified about new episodes.