AI powered robotics for a
safer procedure
Technology. Needleye Robotics is bringing to the market the results of many years of research on AI-powered robotic devices for the diagnosis of prostate cancer. Our product combines the power of AI, machine learning, and robotics to help radiologists and urologists minimizing the diagnostic error and achieve a speedy and effective therapy.
PROBLEM
According to the World Cancer Research Fund, with 1.3 million new cases worldwide, prostate cancer is the second most common cancer in men, causing more than 350,000 deaths each year.
Early diagnosis is crucial to improve the patients’ quality of life and reduce the mortality. When prostate cancer is suspected, diagnosis must be confirmed by a biopsy and subsequent histological analysis.
However, biopsy is far from being accurate, as its mostly manual nature causes a significant diagnostic error that may exceed 30%.
This is due to human cognitive (bad target identification) and manual (bad needle guidance) shortcomings and leads to late or, in the worst case, wrong diagnosis.
To perform a biopsy on suspect tissues, physicians mostly adopt the so-called Freehand Transrectal (TRUS) or, alternatively, Transperineal (TPUS) Ultrasound technique.
Basically, they manually identify the prostate capsule through real-time ultrasound (US) images and overlay MRI to US images to pinpoint the lesion location. Tissue samples are then collected by manually reaching the lesions with a biopsy needle.
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SOLUTION
Needleye platform brings a paradigm shift in biopsy procedures that can turn a subjective and operator-dependent procedure into a safer and more accurate diagnostic tool.
The technology should be able to prove that the biopsy procedure can be done in an ambulatory environment at a reasonable cost, with state-of-the- art accuracy levels.
It allows to treat patients earlier, and its “one–insertion to target” capability makes the procedure predictable and efficient.
It requires minimal learning curve, and it is a truly mobile system. The cognitive and manual functions carried out by the physician are replaced in the Needleye solution by AI algorithms and by robotic motions, but still all decisions are made by the physician.
AI algorithms provide guidance for
Identification of the prostate boundaries and biopsy targets in the MRI images
Fusion of MRI and US images
Real-time processing of US images
Computation of the optimal needle trajectories
The robotic actions are
Computation of the robot positions
Tracking of the needle by the US probe
Robot motion to the entry point
Adjustment of the US probe
Needle positioning and insertion monitoring
This process is repeated until all biopsy targets have been reached.
TECHNICAL DETAILS
The Needleye robot continues the idea to use a SCARA robotic structure to guide a biopsy needle as demonstrated by the first prototype –PROST. The Needleye solution aims at transitioning to the much harder case of realistic anatomical signals, assessing robot’s performance with the complex and noisy signals generated by real-time imaging of human tissues in a realistic environment (TRL 6).
Since the very beginning of the development roadmap, the team has adopted a design thinking approach to the product development, involving physicians (e.g., Prof. Prokar Dasgupta, Prof. Alessandro Antonelli, etc) and value chain partners to collect information on the product features, both in terms of expected performance and for its integration with existing medical equipment.
TECHNICAL DETAILS
The Needleye robot continues the idea to use a SCARA robotic structure to guide a biopsy needle as demonstrated by the first prototype –PROST. The Needleye solution aims at transitioning to the much harder case of realistic anatomical signals, assessing robot’s performance with the complex and noisy signals generated by real-time imaging of human tissues in a realistic environment (TRL 6).
Since the very beginning of the development roadmap, the team has adopted a design thinking approach to the product development, involving physicians (e.g., Prof. Prokar Dasgupta, Prof. Alessandro Micali, etc) and value chain partners to collect information on the product features, both in terms of expected performance and for its integration with existing medical equipment.