Surgical skills modeling in cardiac ablation using deep learning

Cardiovascular diseases, a leading global cause of death, can be treated using Minimally Invasive Surgery (MIS) for various heart conditions. Cardiac ablation is an example of MIS, treating heart rhythm disorders like atrial fibrillation and the operation outcomes are highly dependent on the surgeon's skills. This procedure utilizes catheters, flexible endovascular devices inserted into the patient's blood vessels through a small incision. Traditionally, novice surgeons' performance is assessed in the Operating Room (OR) through surgical tasks. Unskilled behavior can lead to longer operations and inferior surgical outcomes. However, an alternative approach can be capturing surgeons' maneuvers and using them as input for an AI model to evaluate their skills outside the OR. To this end, two experimental setups were proposed to study the skills modelling for surgical behaviours. The first setup simulates the ablation procedure using a mechanical system with a synthetic heartbeat mechanism that measures contact forces between the catheter's tip and tissue. The second one simulates the cardiac catheterization procedure for the surgeon’s practice and records the user's maneuvers at the same time. The first task involved maintaining the force within a safe range while the tip of the catheter is touching the surface. The second task was passing a catheter’s tip through curves and level-intersection on a transparent blood vessel phantom. To evaluate attendees' demonstrations, it is crucial to extract maneuver models for both expert and novice surgeons. Data from participants, including novices and experts, performing the task using the experimental setups, is compiled. Deep recurrent neural networks are employed to extract the model of skills by solving a binary classification problem, distinguishing between expert and novice maneuvers. The results demonstrate the proposed networks' ability to accurately distinguish between novice and expert surgical skills, achieving an accuracy of over 92%

A New Statistical Descriptor for the Physical Characterization and 3D Reconstruction of Heterogeneous Materials

3D reconstruction of heterogeneous materials from 2D images is essential for a precise characterization of their physical properties (mechanical, thermal, electrical, and so on). For this, statistical descriptors such as two-point correlation function (TPCF), lineal path function (LPF), or two-point correlation cluster function (TPCCF) are frequently used. But the effective properties of the reconstructed microstructures are not always corresponding to the real ones as the statistical distribution functions may distribute the material microstructure in a different way from the original one. This is more pronounced for cellular and porous materials such as trabecular bone, fuel cell, and rocks where the connectivity between clusters is not well correlated with the one of real material and degrades the materials physical behavior predictions. This paper proposes a new statistical descriptor, called quality of connection function (QCF), able to determine the quality of connections between clusters and has detailed statistical information about the microstructure distribution. The proposed descriptor is tested on trabecular bone obtained from X-ray micro-computed tomography and used as an example of heterogeneous material having a complex microstructure. Effective properties such as Young’s modulus were calculated for these microstructures and compared with real ones. The new descriptor shows improved capacity to describe the material microstructure distribution and prediction of its physical properties

Statistical Prediction of Bone Microstructure Degradation to Study Patient Dependency in Osteoporosis

Numerical prediction of osteoporosis evolution is a challenging objective in medicine, particularly when one desires to account for patient dependency. The use of statistical methods to reconstruct bone microstructure distribution could be a helpful tool for this prediction, as they are able to provide different types of microstructures that can be optimized to fit with each patient. An initial bone sample was obtained from high-resolution X-ray computed tomography (HRμCT). Its microstructure evolution in time using a previously developed degradation model was used as the ground truth. Statistical bone microstructures were reconstructed at different stages of this evolution using two-point correlation functions (TPCFs). A blind search approach is used to find the optimized statistical microstructures, and the optimized coefficient showed less than 2% TPCF error between the statistical reconstruction and the degraded model. The statistical models also showed less than 13% error in the corresponding mechanical properties. The results showed a good correlation between the developed approach and the ground truth. The method could be extrapolated to account for the physical characterization of patient dependency to predict bone density loss over time

Prediction of bone microstructures degradation during osteoporosis with fuzzy cellular automata algorithm

A novel fuzzy cellular automata is proposed to simulate bone degradation during osteoporosis. The initial three-dimensional (3D) bone microstructure is obtained from computed tomography (CT) images. Cellular automata algorithm is implemented to the 3D lattice and a Sugeno Fuzzy inference system is designed with nine sets of fuzzy rules to simulate the degradation process. A distance vector parameter is defined to describe the number of neighborhood cells that each cell can have a connection with. It is shown that by increasing the value of this distance vector, the results converge toward a quasi-constant degraded microstructure. The obtained microstructure is considered to be the final result and compared to prediction of bone degradation of the literature based on phase exchange calculated from mechanical strain energy. It is shown that the fuzzy cellular automata model predicts a more realistic bone degradation and microstructure distribution than the phase exchange method while having a model significantly simpler

Shape-memory polymer metamaterials based on triply periodic minimal surfaces

Triply periodic minimal surfaces (TPMS) metamaterials and shape-memory polymer (SMP) smart materials are known for their beneficial attributes in novel scientific and industrial fields. Through TPMS designs, low weight accompanied by high surface area are achievable, which are known as crucial parameters in many fields, such as tissue engineering. Moreover, SMPs are well-suited to generate force or to recover their permanent shape by means of an external stimulus. Combining these properties is possible by fabricating TPMS-based metamaterials made out of SMPs, which can be applicable in numerous applications. By considering different level volume fraction of four types of TPMS-based lattices (diamond, gyroid, IWP, and primitive), we focus on the effect of micro-architecture on shape-memory characteristics (i.e., shape recovery, shape fixity, and force recovery) as well as mechanical properties (elastic modulus and Poisson's ratio) of these smart metamaterials. For this purpose, shape-memory effect (SME) is simulated employing thermo-visco-hyperelastic constitutive equations coupled with the time-temperature superposition principle. It is observed that by increasing the level volume fraction of each lattice type, the elastic modulus, shape fixity, and force recovery increase, while the shape recovery diminishes. Such behaviors can be attributed to different deformation modes (flexural or uniaxial) in SMP TPMS-based metamaterials. Furthermore, it is shown that the Poisson's ratio has a nonlinear behavior in these structures. The smart metamaterials introduced in this study have the advantage of providing the possibility of designing implants, especially in bone defects tailored with different micro-architectures depending on each patient's specific need.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Novel Aerospace Material