Estimating Biological Age with AI: A New Frontier in Cancer Care and Beyond

Imagine having a tool that can accurately predict a person’s biological age based on their facial appearance. This might seem like science fiction, but a team of investigators at Mass General Brigham has made this concept a reality using artificial intelligence (AI) technology called FaceAge.

FaceAge uses deep learning algorithms to analyze photographs of individuals and estimate their biological age. The tool was trained on 58,851 photos of presumed healthy individuals from public datasets and tested in a cohort of 6,196 cancer patients from two centers, who had photographs taken at the start of radiotherapy treatment.

The results were striking: patients with cancer appeared significantly older than those without cancer, with an average FaceAge that was about five years older than their chronological age. Moreover, older FaceAge predictions were associated with worse overall survival outcomes across multiple cancer types.

This technology has significant implications for cancer care. By using FaceAge to inform clinical decision-making, healthcare providers may be able to tailor treatment plans more effectively to individual patients based on their estimated biological age and health status.

The researchers also found that FaceAge outperformed clinicians in predicting short-term life expectancies of patients receiving palliative radiotherapy. This highlights the potential for AI-driven tools like FaceAge to provide valuable insights in high-pressure situations, where accurate predictions are crucial.

While further research is needed before this technology can be used in real-world clinical settings, the possibilities are vast and exciting. By expanding its capabilities to predict diseases, general health status, and lifespan, FaceAge has the potential to revolutionize our approach to healthcare.

The co-senior authors of the study, Hugo Aerts, PhD, and Ray Mak, MD, emphasize that this technology opens up new avenues for biomarker discovery from photographs, with implications far beyond cancer care or predicting age. As we increasingly consider chronic diseases as diseases of aging, accurately predicting an individual’s aging trajectory becomes crucial for developing effective treatment plans.

The door to a whole new realm of biomedical innovation has been opened, and it is essential that this technology be developed within a strong regulatory and ethical framework to ensure its safe use in various applications, ultimately helping to save lives.

The MIT engineers’ latest creation is a powerful and lightweight ping pong bot that returns shots with high-speed precision. This table tennis tech has come a long way since the 1980s, when researchers first started building robots to play ping pong. The problem requires a unique combination of technologies, including high-speed machine vision, fast and nimble motors and actuators, precise manipulator control, and accurate real-time prediction.

The team’s new design comprises a multijointed robotic arm that is fixed to one end of a standard ping pong table and wields a standard ping pong paddle. Aided by several high-speed cameras and a high-bandwidth predictive control system, the robot quickly estimates the speed and trajectory of an incoming ball and executes one of several swing types – loop, drive, or chop – to precisely hit the ball to a desired location on the table with various types of spin.

In tests, the engineers threw 150 balls at the robot, one after the other, from across the ping pong table. The bot successfully returned the balls with a hit rate of about 88 percent across all three swing types. The robot’s strike speed approaches the top return speeds of human players and is faster than that of other robotic table tennis designs.

The researchers have since tuned the robot’s reaction time and found the arm hits balls faster than existing systems, at velocities of 20 meters per second. Advanced human players have been known to return balls at speeds of between 21 to 25 meters per second.

“Some of the goal of this project is to say we can reach the same level of athleticism that people have,” Nguyen says. “And in terms of strike speed, we’re getting really, really close.”

The team’s design has several implications for robotics and AI research. It could be adapted to improve the speed and responsiveness of humanoid robots, particularly for search-and-rescue scenarios, or situations where a robot would need to quickly react or anticipate.

This technology also has potential applications in smart robotic training systems. A robot like this could mimic the maneuvers that an opponent would do in a game environment, in a way that helps humans play and improve.

The researchers plan to further develop their system, enabling it to cover more of the table and return a wider variety of shots. This research is supported, in part, by the Robotics and AI Institute.

The Edible Aquatic Robot is a groundbreaking innovation developed by EPFL scientists, who have successfully created a biodegradable and non-toxic device to monitor waterways. This remarkable invention leverages the Marangoni effect, which allows aquatic insects to propel themselves across the surface of water, to create a safe and efficient alternative to traditional environmental monitoring devices made from artificial polymers and electronics.

The robot’s clever design takes advantage of a chemical reaction within a tiny detachable chamber that produces carbon dioxide gas. This gas enters a fuel channel, forcing the fuel out and creating a sudden reduction in water surface tension that propels the robot forward. The device can move freely around the surface of the water for several minutes, making it an ideal solution for monitoring waterways.

What makes this invention even more remarkable is its edible nature. The robot’s outer structure is made from fish food with a 30% higher protein content and 8% lower fat content than commercial pellets. This not only provides strength and rigidity to the device but also acts as nourishment for aquatic wildlife at the end of its lifetime.

The EPFL team envisions deploying these robots in large numbers, each equipped with biodegradable sensors to collect environmental data such as water pH, temperature, pollutants, and microorganisms. The researchers have even fabricated ‘left turning’ and ‘right turning’ variants by altering the fuel channel’s asymmetric design, allowing them to disperse the robots across the water’s surface.

This work is part of a larger innovation in edible robotics, with the Laboratory of Intelligent Systems publishing several papers on edible devices, including edible soft actuators as food manipulators and pet food, fluidic circuits for edible computation, and edible conductive ink for monitoring crop growth. The potential applications of these devices are vast, from stimulating cognitive development in aquatic pets to delivering nutrients or medication to fish.

As EPFL PhD student Shuhang Zhang notes, “The replacement of electronic waste with biodegradable materials is the subject of intensive study, but edible materials with targeted nutritional profiles and function have barely been considered, and open up a world of opportunities for human and animal health.” This groundbreaking innovation in edible aquatic robots has the potential to revolutionize the way we monitor waterways and promote sustainable development.