Official Achievement Announcement

We officially launch i‑Cut Pro, the world's first intelligently‑sensed laparoscopic shaver blade system, marking a revolutionary shift from a "passive tool" to an "active surgical assistant". Integrated with a multi‑mode sensor array in the handle, the system monitors cutting force, vibration spectrum, temperature and tissue impedance in real time, and automatically adjusts operating parameters via artificial intelligence algorithms. Clinical tests show that the intelligent system raises tissue identification accuracy to 96.8%, boosts lesion resection efficiency by 35% while protecting healthy tissues, and signals the formal entry of minimally invasive surgical instruments into a new era of intelligence and precision.

R&D Background & Pain Points

Traditional shaver‑assisted surgery relies on surgeons' tactile perception and experience, with three major uncertainties. First, tissue identification is challenging: edematous, hyperplastic and normal tissues are hard to distinguish visually under arthroscopy, leading to an accidental resection rate of 12–18%. Second, cutting status is unquantifiable: surgeons cannot perceive blade sharpness or load conditions numerically, often resulting in over‑cutting or under‑cutting. Third, parameter settings are experience‑driven: rotational speed, swing amplitude, suction force and other parameters are set empirically without scientific basis.

Studies reveal that improper parameter settings cause 34% of additional tissue damage in complex shoulder arthroscopy. Junior surgeons face a steep learning curve, requiring an average of 50 surgeries to master shaver manipulation skills proficiently.

Core Technological Innovations

• Multi‑Modal Biosensing Fusion TechnologyMiniature fiber‑optic force sensors (0–20 N range, 0.01 N resolution), MEMS accelerometers (5 kHz bandwidth), infrared temperature sensors (±0.2 °C accuracy) and bioimpedance analysis modules (1 kHz–1 MHz frequency range) are integrated into the 6‑mm‑diameter handle. Sensor fusion algorithms calculate real‑time cutting force, tissue hardness, tissue type and blade wear status.
• Adaptive Intelligent Control AlgorithmA tissue‑parameter mapping model is built based on deep learning, outputting optimal operating parameters from sensor inputs. Trained on a dataset of 50 000 surgical videos, the model identifies 12 common tissue types including synovium, cartilage, osteophytes and menisci. The system adjusts parameters every 10 ms to realize dynamic optimization.
• Augmented Reality Surgical Navigation InterfaceA proprietary AR display system is developed to convert sensor data into intuitive visual feedback. Color‑coded tissue boundaries, real‑time cutting‑force bar charts, temperature heat maps and risk alerts are overlaid onto arthroscopic footage. Surgeons can switch display modes via foot switches to achieve seamless eye‑hand‑brain coordination.

Working Mechanism

The core of the intelligent system lies in building a real‑time control loop of sensing‑decision‑execution. At the sensing layer, multi‑sensors collect physical signals; fiber‑optic force sensors measure micro‑strain via the Fabry‑Perot interference principle with a resolution of 0.1 με. At the decision layer, convolutional neural networks extract signal features, completing tissue classification and optimal cutting‑parameter calculation (rotational speed, swing amplitude, suction force) within 1 ms. At the execution layer, a brushless DC motor drive system responds in real time, with rotational‑speed control accuracy of ±50 rpm and a response time of <5 ms.

For high‑risk scenarios (e.g., sudden spikes in cutting force indicating subchondral bone contact), the system triggers alerts while automatically reducing rotational speed by 30%, providing surgeons with a 0.5‑second reaction window and forming a human‑in‑the‑loop (HITL) safety control mode.

Performance Validation

In ex‑vivo tissue experiments, the intelligent system delivers outstanding performance: it achieves 97.3% accuracy in identifying porcine knee joint tissues, with 99.1% specificity for cartilage and 96.8% sensitivity for synovium. In simulated surgeries, the system automatically sets osteophyte resection speed at 4500 rpm (within the conventional empirical range of 3000–6000 rpm), improving resection efficiency by 28% and reducing thermal damage depth by 65%.

A multi‑center randomized controlled trial involving 240 knee arthroscopy patients shows that compared with the conventional blade group: the intelligent blade group reduces intraoperative accidental resection of healthy tissue from 0.82 cm² to 0.21 cm²; the average 6‑month postoperative Lysholm knee score reaches 92.7, significantly higher than the control group's 85.4 (P < 0.01). Subjective surgeon assessments show the intelligent system cuts cutting‑decision time by 40% and mental workload by 35%. Learning‑curve analysis indicates that junior surgeons (<50 surgeries) using the intelligent system achieve 90% of the surgical performance of senior surgeons (>200 surgeries) using conventional techniques.

R&D Strategy & Philosophy

We advocate the design philosophy of intelligence augmentation rather than surgeon replacement, constructing a human‑in‑the‑loop (HITL) intelligent surgical framework. Instead of functioning as a fully automated "robotic surgeon", the system acts as surgeons' sensory extension and decision‑support tool. We establish a three‑tier intelligence architecture: reactive intelligence at the bottom for millisecond‑level safety control, rule‑based intelligence in the middle for parameter recommendations guided by clinical guidelines, and cognitive intelligence at the top for building expert experience models via learning surgical videos from master surgeons.

Meanwhile, we prioritize data security and privacy protection: all patient data is anonymized on‑device, and federated learning frameworks are adopted for model training to keep raw data within hospitals. Interpretability of intelligent algorithms is another key design focus: the system not only provides recommendations but also intuitively displays decision‑making rationales via the AR interface to build trust between engineers and clinicians.

Future Outlook

Intelligent surgical instruments will evolve toward collaboration, networking and personalization. We are developing a multi‑instrument collaborative sensing system that enables shaver blades, radiofrequency blades and suction devices to share sensing data, constructing a digital twin of the surgical field. A 5G edge‑computing architecture is explored to offload partial computing tasks to operating‑room edge servers for lower‑latency real‑time control. Personalized adaptive algorithms are being developed to learn individual surgeons' operating habits within the first 5 minutes of surgery and automatically adjust control‑parameter styles.

By 2029, we will launch an intelligent handle with haptic internet functionality, reproducing tissue texture on surgeons' fingertips via electro‑tactile feedback to realize true virtual haptic perception. In the long run, brain‑computer‑interface‑enabled thought‑controlled manipulation will become feasible, allowing surgeons to precisely control instruments via surgical motion imagery. This will elevate surgical precision to neural‑control levels, ultimately fulfilling the surgical ideal of seamless coordination between mind and hand.