Towards the Feasibility Analysis and Additive Manufacturing of a Novel Flexible Pedicle Screw for Spinal Fixation Procedures

In this paper, we explore the feasibility of developing a novel flexible pedicle screw (FPS) for enhanced spinal fixation of osteoporotic vertebrae. Vital for spinal fracture treatment, pedicle screws have been around since the early 20th century and have undergone multiple iterations to enhance internal spinal fixation. However, spinal fixation treatments tend to be problematic for osteoporotic patients due to multiple inopportune variables. The inherent rigid nature of the pedicle screw, along with the forced linear trajectory of the screw path, frequently leads to the placement of these screws in highly osteoporotic regions of the bone. This results in eventual screw slippage and causing neurological and respiratory problems for the patient. To address this problem, we focus on developing a novel FPS that is structurally capable of safely bending to fit curved trajectories drilled by a steerable drilling robot and bypass highly osteoporotic regions of the vertebral body. Afterwards, we simulate its morphability capabilities using finite element analysis (FEA). We then additively manufacture the FPS using stainless steel (SS) 316L alloy through direct metal laser sintering (DMLS). Finally, the fabricated FPS is experimentally evaluated for its bending performance and compared with the FEA results for verification. Results demonstrate the feasibility of additive manufacturing of FPS using DMLS approach and agreement of the developed FEA with the experiments.

Robot-Enabled Machine Learning-Based Diagnosis of Gastric Cancer Polyps Using Partial Surface Tactile Imaging

In this paper, to collectively address the existing limitations on endoscopic diagnosis of Advanced Gastric Cancer (AGC) Tumors, for the first time, we propose (i) utilization and evaluation of our recently developed Vision-based Tactile Sensor (VTS), and (ii) a complementary Machine Learning (ML) algorithm for classifying tumors using their textural features. Leveraging a seven DoF robotic manipulator and unique custom-designed and additively-manufactured realistic AGC tumor phantoms, we demonstrated the advantages of automated data collection using the VTS addressing the problem of data scarcity and biases encountered in traditional ML-based approaches. Our synthetic-data-trained ML model was successfully evaluated and compared with traditional ML models utilizing various statistical metrics even under mixed morphological characteristics and partial sensor contact.

Towards Biomechanical Evaluation of a Transformative Additively Manufactured Flexible Pedicle Screw for Robotic Spinal Fixation

Vital for spinal fracture treatment, pedicle screw fixation is the gold standard for spinal fixation procedures. Nevertheless, due to the screw pullout and loosening issues, this surgery often fails to be effective for patients suffering from osteoporosis (i.e., having low bone mineral density). These failures can be attributed to the rigidity of existing drilling instruments and pedicle screws forcing clinicians to place these implants into the osteoporotic regions of the vertebral body. To address this critical issue, we have developed a steerable drilling robotic system and evaluated its performance in drilling various J- and U-shape trajectories. Complementary to this robotic system, in this paper, we propose design, additive manufacturing, and biomechanical evaluation of a transformative flexible pedicle screw (FPS) that can be placed in pre-drilled straight and curved trajectories. To evaluate the performance of the proposed flexible implant, we designed and fabricated two different types of FPSs using the direct metal laser sintering (DMLS) process. Utilizing our unique experimental setup and ASTM standards, we then performed various pullout experiments on these FPSs to evaluate and analyze their biomechanical performance implanted in straight trajectories.

A Patient-Specific Framework for Autonomous Spinal Fixation via a Steerable Drilling Robot

In this paper, with the goal of enhancing the minimally invasive spinal fixation procedure in osteoporotic patients, we propose a first-of-its-kind image-guided robotic framework for performing an autonomous and patient-specific procedure using a unique concentric tube steerable drilling robot (CT-SDR). Particularly, leveraging a CT-SDR, we introduce the concept of J-shape drilling based on a pre-operative trajectory planned in CT scan of a patient followed by appropriate calibration, registration, and navigation steps to safely execute this trajectory in real-time using our unique robotic setup. To thoroughly evaluate the performance of our framework, we performed several experiments on two different vertebral phantoms designed based on CT scan of real patients.

Spatial Spinal Fixation: A Transformative Approach Using a Unique Robot-Assisted Steerable Drilling System and Flexible Pedicle Screw

Spinal fixation procedures are currently limited by the rigidity of the existing instruments and pedicle screws leading to fixation failures and rigid pedicle screw pull out. Leveraging our recently developed Concentric Tube Steerable Drilling Robot (CT-SDR) in integration with a robotic manipulator, to address the aforementioned issue, here we introduce the transformative concept of Spatial Spinal Fixation (SSF) using a unique Flexible Pedicle Screw (FPS). The proposed SSF procedure enables planar and out-of-plane placement of the FPS throughout the full volume of the vertebral body. In other words, not only does our fixation system provide the option of drilling in-plane and out-of-plane trajectories, it also enables implanting the FPS inside linear (represented by an I-shape) and/or non-linear (represented by J-shape) trajectories. To thoroughly evaluate the functionality of our proposed robotic system and the SSF procedure, we have performed various experiments by drilling different I-J and J-J drilling trajectory pairs into our custom-designed L3 vertebral phantoms and analyzed the accuracy of the procedure using various metrics.

Order-Based Pre-training Strategies for Procedural Text Understanding

In this paper, we propose sequence-based pretraining methods to enhance procedural understanding in natural language processing. Procedural text, containing sequential instructions to accomplish a task, is difficult to understand due to the changing attributes of entities in the context. We focus on recipes, which are commonly represented as ordered instructions, and use this order as a supervision signal. Our work is one of the first to compare several 'order as-supervision' transformer pre-training methods, including Permutation Classification, Embedding Regression, and Skip-Clip, and shows that these methods give improved results compared to the baselines and SoTA LLMs on two downstream Entity-Tracking datasets: NPN-Cooking dataset in recipe domain and ProPara dataset in open domain. Our proposed methods address the non-trivial Entity Tracking Task that requires prediction of entity states across procedure steps, which requires understanding the order of steps. These methods show an improvement over the best baseline by 1.6% and 7-9% on NPN-Cooking and ProPara Datasets respectively across metrics.