The NIH grant goes toward TransplantAI's work developing more precise models for heart and lung transplantation. Photo via Getty Images

The National Institute of Health has bestowed a Houston medtech company with a $2.2 million Fast-Track to Phase 2 award. InformAI will use the money for the product development and commercialization of its AI-enabled organ transplant informatics platform.

Last year, InformAI CEO Jim Havelka told InnovationMap, “A lot of organs are harvested and discarded.”

TransplantAI solves that problem, as well as organ scarcity and inefficiency in allocation of the precious resource.

How does it work? Machine learning and deep learning from a million donor transplants informs the AI, which determines who is the best recipient for each available organ using more than 500 clinical parameters. Organ transplant centers and organ procurement organizations (OPOs) will be able to use the product to make a decision on how to allocate each organ in real time. Ultimately, the tool will service 250 transplant centers and 56 OPOs around the United States.

The NIH grant goes toward developing more precise models for heart and lung transplantation (kidney and liver algorithms are further along in development thanks to a previous award from the National Science Foundation), as well as Phase 2 efforts to fully commercialize TransplantAI.

"There is an urgent need for improved and integrated predictive clinical insights in solid organ transplantation, such as for real-time assessment of waitlist mortality and the likelihood of successful post-transplantation outcomes," according to the grant’s lead clinical investigator, Abbas Rana, associate professor of surgery at Baylor College of Medicine.

“This information is essential for healthcare teams and patients to make informed decisions, particularly in complex cases where expanded criteria allocation decisions are being considered," Rana continues. "Currently, the separation of donor and recipient data into different systems requires clinical teams to conduct manual, parallel reviews for pairing assessments. Our team, along with those at other leading transplant centers nationwide, receives hundreds of organ-recipient match offers weekly.”

Organ transplantation is moving into the future, and Transplant AI is at the forefront.

InformAI has three AI-based products geared at improving health care. Photo via Getty Images

Fresh off grant, Houston health tech company's AI aims to revolutionize diagnostics, care

data-driven

In Houston, we’re lucky to have top-tier doctors in the Texas Medical Center, ready to treat us with the newest technology. But what about our family members who have to rely on rural hospitals? Thanks to one Houston company, doctors in smaller community hospitals may soon have new tools at their disposal that could improve outcomes for patients around the world.

Since InnovationMap last caught up with Jim Havelka, CEO of InformAI, two years ago, that hope has come far closer to a reality. InformAI is a VC-backed digital health company. Part of JLABS @ TMC innovation facilities, the company uses artificial intelligence to develop both diagnostic tools and clinical outcome predictors. And two of the company’s products will undergo FDA regulatory testing this year.

SinusAI, which helps to detect sinus-related diseases in CT scans, received its CE Mark — the European equivalent of FDA approval — last year and is being sold across the Atlantic today, says Havelka. He adds that in the United States alone, there are roughly 700,000 sinus surgeries that the product is positioned to support.

Another product, RadOnc-AI, is designed to help doctors prescribe radiation dose plans for head and neck cancers.

“Ideally the perfect plan would be to provide radiation to the tumor and nothing around it,” says Havelka. “We’ve built a product, RadOnc-AI, which autogenerates the dose treatment plan based on medical images of that patient.”

It can be an hours-long process for doctors to figure out the path and dose of radiation themselves, but the new product “can build that initial pass in about five minutes,” Havelka says.

That in itself is an exciting development, but because this technology was developed using the expertise of some of the world’s top oncologists, “the first pass plan is in line with what [patients would] get at tier-one institutions,” explains Havelka. This creates “tremendous equity” among patients who can afford to travel to major facilities and those that can’t.

To that end, RadOnc-AI was recently awarded a $1.55 million grant from the Cancer Prevention and Research Institute of Texas, or CPRIT, a state agency that funds cancer research. The Radiological Society of North America announced late last year that InformAI was named an Aunt Minnie Best of Radiology Finalist.

“It’s quite prestigious for our company,” says Havelka. Other recent laurels include InformAI being named one of the 10 most promising companies by the Texas Life Science Forum in November.

And InformAI is only gaining steam. A third product is earlier in its stage of development. TransplantAI will optimize donor organ and patient recipient matches.

“A lot of organs are harvested and discarded,” Havelka says.

His AI product has been trained on a million donor transplants to help determine who is the best recipient for an organ. It even takes urgency into account, based on a patient’s expected mortality within 90 days. The product is currently a fully functional prototype and will soon move through its initial regulatory clearances.

The company — currently backed by three VC funds, including DEFTA Partners, Delight Ventures, and Joyance Partners — is planning to do another seed round in Q2 of 2023.

“We’ve been able to get recognized for digital health products that can be taken to market globally,” says Havelka.

But what he says he’s most excited about is the social impact of his products. With more money raised, InformAI will be able to speed up development of additional products, including expanding the cancers that the company will be targeting. And with that, more and more patients will one day be treated with the highest level of care.

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Houston food giant Sysco to acquire competitor in $29 billion deal

Mergers & Acquisitions

Sysco, the nation's largest food distributor, will acquire supplier Restaurant Depot in a deal worth more than $29 billion.

The acquisition would create a closer link between Sysco and its customers that right now turn to Restaurant Depot for supplies needed quickly in an industry segment known as “cash-and-carry wholesale.”

Sysco, based in Houston, serves more than 700,000 restaurants, hospitals, schools, and hotels, supplying them with everything from butter and eggs to napkins. Those goods are typically acquired ahead of time based on how much traffic that restaurants typically see.

Restaurant Depot offers memberships to mom-and-pop restaurants and other businesses, giving them access to warehouses stocked with supplies for when they run short of what they've purchased from suppliers like Sysco.

It is a fast growing and high-margin segment that will likely mean thousands of restaurants will rely increasingly on Sysco for day-to-day needs.

Restaurant Depot shareholders will receive $21.6 billion in cash and 91.5 million Sysco shares. Based on Sysco’s closing share price of $81.80 as of March 27, 2026, the deal has an enterprise value of about $29.1 billion.

Restaurant Depot was founded in Brooklyn in 1976. The family-run business then known as Jetro Restaurant Depot, has become the nation's largest cash-and-carry wholesaler.

The boards of both companies have approved the acquisition, but it would still need regulatory approval.

Shares of Sysco Corp. tumbled 13% Monday to $71.26, an initial decline some industry analysts expected given the cost of the deal.

Houston researcher builds radar to make self-driving cars safer

eyes on the road

A Rice University researcher is giving autonomous vehicles an “extra set of eyes.”

Current autonomous vehicles (AVs) can have an incomplete view of their surroundings, and challenges like pedestrian movement, low-light conditions and adverse weather only compound these visibility limitations.

Kun Woo Cho, a postdoctoral researcher in the lab of Rice professor of electrical and computer engineering Ashutosh Sabharwal, has developed EyeDAR to help address such issues and enhance the vehicles’ sensing accuracy. Her research was supported in part by the National Science Foundation.

The EyeDAR is an orange-sized, low-power, millimeter-wave radar that could be placed at streetlights and intersections. Its design was inspired by that of the human eye. Researchers envision that the low-cost sensors could help ensure that AVs always pick up on emergent obstacles, even when the vehicles are not within proper range for their onboard sensors and when visibility is limited.

“Current automotive sensor systems like cameras and lidar struggle with poor visibility such as you would encounter due to rain or fog or in low-lighting conditions,” Cho said in a news release. “Radar, on the other hand, operates reliably in all weather and lighting conditions and can even see through obstacles.”

Signals from a typical radar system scatter when they encounter an obstacle. Some of the signal is reflected back to the source, but most of it is often lost. In the case of AVs, this means that "pedestrians emerging from behind large vehicles, cars creeping forward at intersections or cyclists approaching at odd angles can easily go unnoticed," according to Rice.

EyeDAR, however, works to capture lost radar reflections, determine their direction and report them back to the AV in a sequence of 0s and 1s.

“Like blinking Morse code,” Cho added. “EyeDAR is a talking sensor⎯it is a first instance of integrating radar sensing and communication functionality in a single design.”

After testing, EyeDAR was able to resolve target directions 200 times faster than conventional radar designs.

While EyeDAR currently targets risks associated with AVs, particularly in high-traffic urban areas, researchers also believe the technology behind it could complement artificial intelligence efforts and be integrated into robots, drones and wearable platforms.

“EyeDAR is an example of what I like to call ‘analog computing,’” Cho added in the release. “Over the past two decades, people have been focusing on the digital and software side of computation, and the analog, hardware side has been lagging behind. I want to explore this overlooked analog design space.”