Assessment of 3D Printing for Surgical Instrument Manufacturing

Three-dimensional (3D) printing has emerged as a method for rapid prototyping and manufacturing of tools. In low-resource settings or field settings, the ability to perform surgeries is often limited by a lack of surgical instruments. On-demand manufacture of surgical instruments via 3D printing may offer a low-cost, reliable, convenient solution for provision of necessary care, particularly during trauma or emergency situations. The global coronavirus-19 disease pandemic has emphasized the need for rapid manufacturing of surgical instruments at the point of care, as the pandemic has often limited patient access to hospitals, due to measures to minimize the spread of infectious disease. Moreover, the ability to 3D print surgical instruments is a priority for enabling surgery during space missions. Recent progress has been made on 3D printing of commonly used surgical instruments from plastics. Important surgical tools such as forceps, scalpel handles, needle drivers, Army/Navy retractors, and hemostats have all been 3D printed, with typical print times on the order of hours. This paper assesses the current status of 3D printing of surgical instruments. The review will include 3D printing methods, raw materials, design times, print times, sterilization methods, and the types of surgical instruments that have been successfully printed. In addition, the results of mechanical testing and simulated surgical testing of 3D printed surgical instruments will be described. Finally, avenues for future work will be identified, including the need for faster print times, and the necessity for producing more intricate instruments via 3D printing. © 2022, Avestia Publishing. All rights reserved.

Synthetic data accelerates the development of generalizable learning-based algorithms for X-ray image analysis

Artificial intelligence (AI) now enables automated interpretation of medical images. However, AI's potential use for interventional image analysis remains largely untapped. This is because the post hoc analysis of data collected during live procedures has fundamental and practical limitations, including ethical considerations, expense, scalability, data integrity and a lack of ground truth. Here we demonstrate that creating realistic simulated images from human models is a viable alternative and complement to large-scale in situ data collection. We show that training AI image analysis models on realistically synthesized data, combined with contemporary domain generalization techniques, results in machine learning models that on real data perform comparably to models trained on a precisely matched real data training set. We find that our model transfer paradigm for X-ray image analysis, which we refer to as SyntheX, can even outperform real-data-trained models due to the effectiveness of training on a larger dataset. SyntheX provides an opportunity to markedly accelerate the conception, design and evaluation of X-ray-based intelligent systems. In addition, SyntheX provides the opportunity to test novel instrumentation, design complementary surgical approaches, and envision novel techniques that improve outcomes, save time or mitigate human error, free from the ethical and practical considerations of live human data collection.Simulated data is an alternative to real data for medical applications where interventional data are needed to train AI-based systems. Gao and colleagues develop a model transfer paradigm to train deep networks on synthetic X-ray data and corresponding labels generated using simulation techniques from CT scans. The approach establishes synthetic data as a viable resource for developing machine learning models that apply to real clinical data.

Development of a "Scissor-Tip-Separator" for adjustment of scissor blade separation and prevention of scissor blade damage during steam sterilization.

BACKGROUND: Reprocess reusable surgical instruments during steam sterilization; damage occurs to sharp scissor blades in close position, so steam cannot reach the blades. Surgical instruments' management requires standards to ensure patient safety and prevent harmful pathogens, especially in the COVID-19 pandemic. Although various devices can separate scissor blades, they do not prevent damage to cutting edges. To address the above problem, we developed a new scissor protector, the "Scissor-Tip-Separator," and evaluated its efficacy. METHODS: The "Scissor-Tip-Separator" design follows the steam sterilization guideline that instrument tips must be separated. The locking handles and V groove mechanism keep the scissor blades separated while preventing damage to the cutting edges. For efficacy assessment, purposive sampling was performed to select 44 Thai perioperative nurses at Ramathibodi Hospital, Bangkok, Thailand, to evaluate the "Scissor-Tip-Separators" in 450 sterile instrument containers. All participants evaluated surgical scissors placed in the "Scissor-Tip-Separators" during instrument setup, following a problem record checklist. At the end of the fifth use, participants were asked to complete the "Scissor-Tip-Separator" Effectiveness Scale, which was used to test the structural design of the "Scissor-Tip-Separator" in terms of function, usability, and safety. The Adenosine Triphosphate surface test was also used to validate the "Scissor-Tip-Separator" cleanliness. Data were collected from August 2020 to November 2020, then analyzed via descriptive statistics. RESULTS: The "Scissor-Tip-Separator" met the cleaning validation criteria, and in 44 uses, the physical property remained the same. The scissor shank was discovered loose from the handle before it had been unlocked (0.2-0.4%) at the 45th use. Based on participants' opinions, the overall instrument effectiveness was high in terms of function, usability, and safety. CONCLUSION: The "Scissor-Tip-Separator" regulates scissor blade separation under sterilization guidelines; it prevents damage to cutting edges, thus ensuring patient safety. It protects against losses in a sterile field and can prevent hand injuries.

Transfer Learning from Healthy to Unhealthy Patients for the Automated Classification of Functional Brain Networks in fMRI

Functional Magnetic Resonance Imaging (fMRI) is an essential tool for the pre-surgical planning of brain tumor removal, which allows the identification of functional brain networks to preserve the patient’s neurological functions. One fMRI technique used to identify the functional brain network is the resting-state-fMRI (rs-fMRI). This technique is not routinely available because of the necessity to have an expert reviewer who can manually identify each functional network. The lack of sufficient unhealthy data has so far hindered a data-driven approach based on machine learning tools for full automation of this clinical task. In this article, we investigate the possibility of such an approach via the transfer learning method from healthy control data to unhealthy patient data to boost the detection of functional brain networks in rs-fMRI data. The end-to-end deep learning model implemented in this article distinguishes seven principal functional brain networks using fMRI images. The best performance of a 75% correct recognition rate is obtained from the proposed deep learning architecture, which shows its superiority over other machine learning algorithms that were equally tested for this classification task. Based on this best reference model, we demonstrate the possibility of boosting the results of our algorithm with transfer learning from healthy patients to unhealthy patients. This application of the transfer learning technique opens interesting possibilities because healthy control subjects can be easily enrolled for fMRI data acquisition since it is non-invasive. Consequently, this process helps to compensate for the usual small cohort of unhealthy patient data. This transfer learning approach could be extended to other medical imaging modalities and pathology.

Remanufacturing of single-use medical devices: a case study on cross-border collaboration between the UK and Nigeria.

This paper aims to evaluate the current state of the remanufacturing of medical devices, considering the differences between developed and developing countries. With reference to various socio-economic factors, the impact of remanufacturing to sustainability was evaluated and from this, single-use medical devices were deemed to be critical in minimising waste within the medical industry. This is even more critical with increasing use of single-use devices in the Coronavirus disease 2019 (COVID 19) pandemic. It was identified that cleaning is a key consideration for ensuring a safe remanufacturing process that would minimise the risk of infection to patients. Therefore, this process was evaluated and appropriate recommendations made. Although there may be some challenges, further research would be required for integration of the methodology and process outlined into the medical sector.

Electro-fluid-mechanics of the heart

This article presents an overview of the dynamics of the human heart and the main goal is the discussion of its fluid mechanic features. We will see, however, that the flow in the heart can not be fully described without considering its electrophysiology and elastomechanics as well as the interaction with the systemic and pulmonary circulations with which it is strongly connected. Biologically, the human heart is similar to that of all warm-blooded mammals and it satisfies the same allometric laws. Since the Paleolithic Age, however, humans have improved their living conditions, have modified the environment to satisfy their needs and, more recently, have developed advanced medical knowledge which has allowed triple the number of heartbeats with respect to other mammals. In the last century, effective diagnostic tools, reliable surgical procedures and prosthetic devices have been developed and refined leading to substantial progress in cardiology and heart surgery with routine clinical practice which nowadays cures many disorders, once lethal. Pulse duplicators have been built to reproduce the pulsatile flow and ‘blood analogues’, have been realized. Heart phantoms, can attain deformations similar to the real heart although the active contraction and the tissue anisotropy still can not be replicated. Numerical models have also become a viable alternative for cardiovascular research: they do not suffer from limitations of material properties and device technologies, thus making possible the realization of truly digital twins. Unfortunately, a high-fidelity model for the whole heart consists of a system of coupled, nonlinear partial differential equations with a number of degrees of freedom of the order of a billion and computational costs become the bottleneck. An additional challenge comes from the inherent human variability and the uncertainty of the heart parameters whose statistical assessment requires a campaign of simulations rather than a single deterministic calculation;reduced and surrogate models can be employed to alleviate the huge computational burden and all possibilities are currently being pursued. In the era of big data and artificial intelligence, cardiovascular research is also advancing by exploiting the latest technologies: equation-based augmented reality, virtual surgery and computational prediction of disease progression are just a few examples among many that will become standard practice in the forthcoming years.

Addressing The Post Covid-19 Economic Consequences Of Lockdown In India

On account of the national closedown, the economy has suffered a severe slump. The reduced purchase of goods and services has lead to slow down of domestic manufacturing and agricultural sectors, leading to severe unemployment in urban industrial areas and contraction of the rural economy. This lockdown has influenced economy and business circumstance in India as it has occurred in other nations. The ongoing lockdown due to COVID-19 outbreak affects the Indian economy in many ways, including sharp declines in domestic demand, lower tourism and business travel, trade and production linkages, supply disruptions, and health effects. There are certain sectors that have been affected due to the outbreak of corona virus. This research paper, anticipates indicative measures that may be taken to revive the economy as well as affected sectors to some extent. First, this paper provides an overview of effect of this lockdown on Indian economy. With no manufacturing activity, it is likely that growth of gross domestic product (GDP) will be slowed down. This will be followed listing of the sectors affected due to COVID-19, which carries the risk of global supply chain disruptions. This includes five import items that are heavily dependent on China - electrical machinery, mechanical appliances, organic chemicals, plastics and surgical instruments - that make up about 28% of India's import basket could be the mostly affected ones due to this potential shutdown. Next, the paper will present some of the indicative measures to revive the economy and rejuvenate the affected sectors. Protection of workers at the workplace must be given utmost priority followed by adapting to new work arrangements such as work-from-home (WFH). This will be followed by measures to stimulate the economy and labour demand by making and implementing active fiscal policy. Making expenditure on purchasing domestic goods and services will be the best way to push the economy. As a matter of fact, it is now may be required to evaluate on what can be produced here (in India) and give a deliberate thought to it towards implanting the same. Further, India will be at advantage by having domestically based and well established 'pharma industry'. This sector may act as 'catalyst sector' towards economic growth of the country.