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The AI-robot system for endoscopic bone surgery consists of an artificial intelligence surgical navigation system and a bone endoscopic surgical robot system.
Artificial intelligence surgical navigation systems learn ultrasound, MR, and endoscopic image information through deep learning and provide refined information to the medical staff,
The bone endoscopic surgical robot system supports precise and minimal invasive surgery by providing a therapeutic and endoscopic arm, which involves remote controlled and active steering operations possible.
Existing osteocystoma surgery has a large incision and requires a lot of radiographs, which eliminates the risk for the patient and medical staff.
Applying AI based on ultrasound and MR images data will reduce radiation exposure and
minimal invasive surgery by a flexible endoscopic surgical robot, leading to a safe and accurate surgery.
・Application of ultrasound and MR images,
which were mainly used for soft tissues,
for artificial intelligence learning in musculoskeletal surgery ・Detection of abnormal tissue in endoscopic images that are difficult to identify
: Reducing radiation exposure.
: Accurate skin incision and bone hole creation possible.
・Flexible endoscopic surgical robot with
sufficient rigidity and flexibility for
: Minimally invasive surgery inside
and outside the bone is possible.
: Safe And Accurate Surgery.
The skin incision range was measured in a total of 4 surgeries, and the results are shown in [Table 1].
In the case of
conventional surgeries, the incision range was 13.8 cm. however, the maximum incision range was 3.87cm on average
which decreased by approximately 71% in the case of robotic surgeries.
(unit : cm)
|Case Number||Type of operation||Incision range|
|Average of Robotic Surgery||3.87±0.25|
|Average of total surgeries||6.35±4.3|
In a total of 4 surgeries, the number of intraoperative C-arm imaging was recorded, and the results are shown in [Table 2].
A total of 16 C-arm images were considered in conventional surgeries, however, an average of 7 times implies a decrease of approximately 56% was considered in robotic surgeries.
|Case number||Type of surgery||Number of shots|
|The average number of imaging for robotic surgery||9.3±8.3|
|The total average number of imaging||7±8.5|
<Bone Endoscopic Robotic Arm>
The bone endoscopic surgical robot system comprised the bone endoscopic robot arm, robot arm manipulator, and end effector. Rigid enough to be used as an end-effector that cuts bones, such as burs or drills. Flexibility to be used in narrow bone space during surgery Channels optimized for use with end-effectors Quick and easy exchange of end-effectors Robot arm base easy to link with the surgical path guide device.
<Robot Arm Controller (BE Controller)>
A control device that can control two bone endoscope robot arms simultaneously. Intuitive and ergonomic design that does not fatigue even after prolonged use. The manipulating device for controlling the operation of the end effector.
Burr, that can transmit rotational power using a bent bone endoscopic robot arm. A flexible endoscope that can enter a bent bone endoscopic robot arm. Saline perfusion device that can provide safe perfusion pressure while cleaning the inside of the bone. The injector of Demineralized Bone Matrix (DBM) Forceps to pick up bone fragments or osteotomy.
The artificial intelligence surgical navigation system comprises artificial intelligence surgery planning and surgical route guidance devices. Detection of bone contours from ultrasound images using artificial intelligence learning The ultrasound image is collected using a surgical route guide device.At this time, the ultrasound image and the position/posture information of the ultrasound probe are simultaneously recorded. 3D Image is reconstructed using this information. Detection of bone contours and lesion locations in MR images using artificial intelligence learning Providing 3D location information of bones and lesions by matching the reconstructed 3D ultrasound image and MR image. Establish a path plan for entering the bone endoscopic robot arm based on the above information. Detecting the presence of abnormal tissue in endoscopic images using artificial intelligence learning
A route guidance device consists of two manual arms each capable of measuring position/posture with more than 5 degrees of freedom The manual arm can operate Remote of Center (RCM). Locking mechanism to stop the manual arm in the desired position Detachable sleeve for insertion of the bone endoscopic robot arm As the manual arm moves, the insertion position of the bone endoscopic robot arm is transmitted and displayed in real-time on the artificial intelligence surgery planning device. The ultrasonic probe can be detached and the ultrasonic image and probe location information are synchronized.
Ultrasound scan of bone surface
bone surface / 3D reconstruction of MR image
Matching ultrasound image and MR image
Insertion of bone endoscopic surgical robot
Lesion removal using a bone drill
Cleaning through perfusion
Injection of bone morphogenic inducers
Removal of the bone endoscopic surgical robot