Towards retrieving force feedback in robotic-assisted surgery: a supervised neuro-recurrent-vision approach

Robotic-assisted minimally invasive surgeries have gained a lot of popularity over conventional procedures as they offer many benefits to both surgeons and patients. Nonetheless, they still suffer from some limitations that affect their outcome. One of them is the lack of force feedback which restricts the surgeon's sense of touch and might reduce precision during a procedure. To overcome this limitation, we propose a novel force estimation approach that combines a vision based solution with supervised learning to estimate the applied force and provide the surgeon with a suitable representation of it. The proposed solution starts with extracting the geometry of motion of the heart's surface by minimizing an energy functional to recover its 3D deformable structure. A deep network, based on a LSTM-RNN architecture, is then used to learn the relationship between the extracted visual-geometric information and the applied force, and to find accurate mapping between the two. Our proposed force estimation solution avoids the drawbacks usually associated with force sensing devices, such as biocompatibility and integration issues. We evaluate our approach on phantom and realistic tissues in which we report an average root-mean square error of 0.02 N.Peer ReviewedPostprint (author's final draft

Optimising surface roughness and density in titanium fabrication via laser powder bed fusion

The Ti6Al4V alloy has many advantages, such as being lightweight, formal, and resistant to corrosion. This makes it highly desirable for various applications, especially in the aerospace industry. Laser Powder Bed Fusion (LPBF) is a technique that allows for the production of detailed and unique parts with great flexibility in design. However, there are challenges when it comes to achieving high-quality surfaces and porosity formation in the material, which limits the wider use of LPBF. To tackle these challenges, this study uses statistical techniques called Design of Experiments (DoE) and Analysis of Variance (ANOVA) to investigate and optimise the process parameters of LPBF for making Ti6Al4V components with improved density and surface finish. The parameters examined in this study are laser power, laser scan speed, and hatch space. The optimisation study results show that using specific laser settings, like a laser power of 175 W, a laser scan speed of 1914 mm/s, and a hatch space of 53 µm, produces Ti6Al4V parts with a high relative density of 99.54% and low top and side surface roughness of 2.6 µm and 4.3 µm, respectively. This promising outcome demonstrates the practicality of optimising Ti6Al4V and other metal materials for a wide range of applications, thereby overcoming existing limitations and further expanding the potential of LPBF while minimising inherent process issues

Multi stages toolpath optimisation of single point incremental forming process

Single point incremental forming (SPIF) is a flexible technology that can form a wide range of sheet metal products without the need for using punch and die sets. As a relatively cheap and die-less process, this technology is preferable for small and medium customised production. However, the SPIF technology has drawbacks, such as the geometrical inaccuracy and the thickness uniformity of the shaped part. This research aims to optimise the formed part geometric accuracy and reduce the processing time of a two-stage forming strategy of SPIF. Finite element analysis (FEA) was initially used and validated using experimental literature data. Furthermore, the design of experiments (DoE) statistical approach was used to optimise the proposed two-stage SPIF technique. Mass scaling technique was applied during the finite element analysis to minimise the computational time. The results showed that the step size during forming stage two have significantly affected the geometrical accuracy of the part, whereas the forming depth during stage one was insignificant to the part quality. It was also revealed that the geometrical improvement had taken place along the base and the wall regions. However, the areas near the clamp system showed minor improvements. The optimised two-stage strategy had successfully decreased both the geometrical inaccuracy and processing time. After optimisation, the average values of the geometrical deviation and forming time were reduced by 25% and 55.56%, respectively

Hybrid finite element–smoothed particle hydrodynamics modelling for optimizing cutting parameters in CFRP composites

Carbon-fibre-reinforced plastic (CFRP) is increasingly being used in various applications including aerospace, automotive, wind energy, sports, and robotics, which makes the precision modelling of its machining operations a critical research area. However, the classic finite element modelling (FEM) approach has limitations in capturing the complexity of machining, particularly with regard to the interaction between the fibre–matrix interface and the cutting edge. To overcome this limitation, a hybrid approach that integrates smoothed particle hydrodynamics (SPHs) with FEM was developed and tested in this study. The hybrid FEM-SPH approach was compared with the classic FEM approach and validated with experimental measurements that took into account the cutting tool’s round edge. The results showed that the hybrid FEM-SPH approach outperformed the classic FEM approach in predicting the thrust force and bounce back of CFRP machining due to the integrated cohesive model and the element conversion after failure in the developed approach. The accurate representation of the fibre–matrix interface in the FEM-SPH approach resulted in predicting precise chip formation in terms of direction and morphology. Nonetheless, the computing time of the FEM-SPH approach is higher than the classic FEM. The developed hybrid FEM-SPH model is promising for improving the accuracy of simulation in machining processes, combining the benefits of both techniques

Designing lightweight 3D-printable bioinspired structures for enhanced compression and energy absorption properties

Recent progress in additive manufacturing, also known as 3D printing, has offered several benefits, including high geometrical freedom and the ability to create bioinspired structures with intricate details. Mantis shrimp can scrape the shells of prey molluscs with its hammer-shaped stick, while beetles have highly adapted forewings that are lightweight, tough, and strong. This paper introduces a design approach for bioinspired lattice structures by mimicking the internal microstructures of a beetle’s forewing, a mantis shrimp’s shell, and a mantis shrimp’s dactyl club, with improved mechanical properties. Finite element analysis (FEA) and experimental characterisation of 3D printed polylactic acid (PLA) samples with bioinspired structures were performed to determine their compression and impact properties. The results showed that designing a bioinspired lattice with unit cells parallel to the load direction improved quasi-static compressive performance, among other lattice structures. The gyroid honeycomb lattice design of the insect forewings and mantis shrimp dactyl clubs outperformed the gyroid honeycomb design of the mantis shrimp shell, with improvements in ultimate mechanical strength, Young’s modulus, and drop weight impact. On the other hand, hybrid designs created by merging two different designs reduced bending deformation to control collapse during drop weight impact. This work holds promise for the development of bioinspired lattices employing designs with improved properties, which can have potential implications for lightweight high-performance applications

Chip formation and orthogonal cutting optimisation of unidirectional carbon fibre composites

This study presents a thorough experimental investigation utilising the design of experiments and analysis of variance (ANOVA) to examine the impact of machining process parameters on chip formation mechanisms, machining forces, workpiece surface integrity, and damage resulting from the orthogonal cutting of unidirectional CFRP. The study identified the mechanisms behind chip formation and found it to significantly impact the workpiece orientation of fibre and the tool’s cutting angle, resulting in increased fibre bounceback at larger fibre orientation angles and when using smaller rake angle tools. Increasing the depth of cut and fibre orientation angle results in an increased damage depth, while using higher rake angles reduces it. An analytical model based on response surface analysis for predicting machining forces, damage, surface roughness, and bounceback was also developed. The ANOVA results indicate that fibre orientation is the most significant factor in machining CFRP, while cutting speed is insignificant. Increasing fibre orientation angle and depth leads to deeper damage, while larger tool rake angles re-duce damage. Machining workpieces with 0° fibre orientation angle results in the least subsurface damage, and surface roughness is unaffected by the tool rake angle for fibre orientations between 0° to 90° but worsens for angles greater than 90°. Optimisation of cutting parameters were subsequently optimised to improve machined workpiece surface quality and reduce forces. The experimental results showed that negative rake angle and cutting at moderately low speeds (366 mm/min) is the optimal conditions for machining laminates with a fibre angle of θ = 45°. On the other hand, for composite materials with fibre angles of θ = 90° and θ = 135°, it is recommended to use a high positive rake angle and cutting speeds

Sensory substitution for force feedback recovery: A perception experimental study

Robotic-assisted surgeries are commonly used today as a more efficient alternative to traditional surgical options. Both surgeons and patients benefit from those systems, as they offer many advantages, including less trauma and blood loss, fewer complications, and better ergonomics. However, a remaining limitation of currently available surgical systems is the lack of force feedback due to the teleoperation setting, which prevents direct interaction with the patient. Once the force information is obtained by either a sensing device or indirectly through vision-based force estimation, a concern arises on how to transmit this information to the surgeon. An attractive alternative is sensory substitution, which allows transcoding information from one sensory modality to present it in a different sensory modality. In the current work, we used visual feedback to convey interaction forces to the surgeon. Our overarching goal was to address the following question: How should interaction forces be displayed to support efficient comprehension by the surgeon without interfering with the surgeon’s perception and workflow during surgery? Until now, the use the visual modality for force feedback has not been carefully evaluated. For this reason, we conducted an experimental study with two aims: (1) to demonstrate the potential benefits of using this modality and (2) to understand the surgeons’ perceptual preferences. The results derived from our study of 28 surgeons revealed a strong positive acceptance of the users (96%) using this modality. Moreover, we found that for surgeons to easily interpret the information, their mental model must be considered, meaning that the design of the visualizations should fit the perceptual and cognitive abilities of the end user. To our knowledge, this is the first time that these principles have been analyzed for exploring sensory substitution in medical robotics. Finally, we provide user-centered recommendations for the design of visual displays for robotic surgical systems.Peer ReviewedPostprint (author's final draft

Hospitalisations related to administration errors of psychotropic drugs: a nationwide retrospective study between 1998 and 2019 in Australia

Objectives: Medication administration error occurs when there is a discrepancy between what the patient received or was planned to receive and what the doctor originally intended. The aim of this study was to examine the trends in hospitalisation related to administration errors of psychotropic drugs in Australia.Materials and Methods: This was a secular trend analysis study that examined the hospitalisation pattern for medication administration errors of psychotropic drugs in Australia between 1998 and 2019. Data on medication administration errors of psychotropic drugs was obtained from The National Hospital Morbidity Database. We analysed the variation in hospitalisation rates using the Pearson chi-square test for independence.Results: Hospitalisation rates related to administration errors of psychotropic drugs increased by 8.3% [from 36.22 (95% CI 35.36—37.08) in 1998 to 39.21 (95% CI 38.44—39.98) in 2019 per 100,000 persons, p < 0.05]. Overnight-stay hospital admission patients accounted for 70.3% of the total number of episodes. Rates of same-day hospitalisation increased by 12.3% [from 10.35 (95% CI 9.90—10.81) in 1998 to 11.63 (95% CI 11.21—12.05) in 2019 per 100,000 persons]. Rates of overnight-stay hospital admission increased by 1.8% [from 25.86 (95% CI 25.13—26.59) in 1998 to 26.34 (95% CI 25.71—26.97) in 2019 per 100,000 persons]. Other and unspecified antidepressants (selective serotonin and norepinephrine reuptake inhibitors) were the most common reason for hospitalisation accounting for 36.6% of the total number of hospitalisation episodes. Females accounted for 111,029 hospitalisation episodes, representing 63.2% of all hospitalisation episodes. The age group 20–39 years accounted for nearly half (48.6%) of the total number of episodes.Conclusion: Psychotropic drug administration error is a regular cause of hospitalization in Australia. Hospitalizations usually required overnight stays. The majority of hospitalizations were in persons aged 20–39 years, which is concerning and warrants further investigation. Future studies should examine the risk factors for hospitalization related to psychiatric drug administration errors

Breast-cancer detection using blood-based infrared molecular fingerprints

BACKGROUND Breast cancer screening is currently predominantly based on mammography, tainted with the occurrence of both false positivity and false negativity, urging for innovative strategies, as effective detection of early-stage breast cancer bears the potential to reduce mortality. Here we report the results of a prospective pilot study on breast cancer detection using blood plasma analyzed by Fourier-transform infrared (FTIR) spectroscopy - a rapid, cost-effective technique with minimal sample volume requirements and potential to aid biomedical diagnostics. FTIR has the capacity to probe health phenotypes via the investigation of the full repertoire of molecular species within a sample at once, within a single measurement in a high-throughput manner. In this study, we take advantage of cross-molecular fingerprinting to probe for breast cancer detection. METHODS We compare two groups: 26 patients diagnosed with breast cancer to a same-sized group of age-matched healthy, asymptomatic female participants. Training with support-vector machines (SVM), we derive classification models that we test in a repeated 10-fold cross-validation over 10 times. In addition, we investigate spectral information responsible for BC identification using statistical significance testing. RESULTS Our models to detect breast cancer achieve an average overall performance of 0.79 in terms of area under the curve (AUC) of the receiver operating characteristic (ROC). In addition, we uncover a relationship between the effect size of the measured infrared fingerprints and the tumor progression. CONCLUSION This pilot study provides the foundation for further extending and evaluating blood-based infrared probing approach as a possible cross-molecular fingerprinting modality to tackle breast cancer detection and thus possibly contribute to the future of cancer screening