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Develop AI Algorithms that Improve Patients’ Lives

Are you looking for a high-impact problem to solve? You are in the right place. We have the high-impact, implementable problems you’ve been searching for. Algorithms based on TOUCH-AI use cases will not only assist the radiology community in the delivery of high-quality patient care, they have the potential to come to market more quickly. Get started transforming health care today!

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What Are TOUCH-AI Use Cases?

TOUCH-AI use cases are scenarios where the use of artificial intelligence (AI) may help improve medical imaging care. Each TOUCH-AI use case provides narrative descriptions and flow charts which specify the health care goal of the algorithm, the required clinical input, how it should integrate into the clinical workflow and how it will interface with users and tools.

Why Are TOUCH-AI Use Cases Special?

To date, no other national health care organization has developed use cases that allow developers to create AI algorithms that assist health care professionals. These first-of-their-kind use cases facilitate the development and implementation of AI applications that will help radiology professionals in disease detection, characterization and treatment.

TOUCH-AI use cases also simplify deployment and validation. The standardized terminology results in algorithms compatible with other radiology tools, which are more easily integrated into clinical workflow. TOUCH-AI use cases were also designed to smooth the validation process by providing clearly defined benchmarks which can be used to demonstrate performance to purchasers and to the FDA.

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Example Use Cases

LV Functional Assessment (for conditions like heart disease)

Heart disease is the leading cause of death in the United States — claiming more than 600,000 lives each year. This use case will help developers produce algorithms that provide fast, accurate measurements of the most important factors in heart healthiness. This gives clinicians a reliable and quick indicator to a patient’s heart health which holds promise for better patient care over time.

Suspicious Breast Microcalcification Analysis (for breast cancer)

Although breast calcifications are usually noncancerous, certain patterns of calcifications — such as tight clusters with irregular shapes — may indicate breast cancer or precancerous changes to breast tissue. Algorithms resulting from this use case would automatically classify microcalcifications to help physicians better determine which patients may need to return for further testing. This can help prevent “false positive” exams and help patients avoid unwarranted procedures (including biopsies), with less risk of underdiagnoses.

Scoliosis Detection

More than 4 million Americans are stricken with scoliosis. It remains the most common spinal deformity in school-age children. This use case will aid developers in creating algorithms that speed capture and analysis of common relevant metrics that are typically time-intensive to do manually. Though metrics like the Cobb angle are commonly reported today, they have their limitations in accounting for vertebrae rotation. So the use case also defines a series of secondary metrics that would best help radiologists call and treat scoliosis. This can help avoid variance in reporting and managing scoliosis patients.

Pneumothorax Detection (collapsed lung)

Trauma, cigarette smoking, drug abuse and certain lung diseases can lead to a “tension” (fully) or “simple” (partially) collapsed lung. This condition — which strikes nearly 74,000 Americans each year — can be deadly if not diagnosed quickly and accurately. Algorithms resulting from this use case can be used to speed diagnosis and intervention from a variety of end points — including prioritizing worklists, queuing up diagnostic tools for radiologists and alerting referring providers of the condition.