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Machine Learning for Designing a Mechanical Ventilator:
Published in Pushpa Singh, Divya Mishra, Kirti Seth, Transformation in Healthcare with Emerging Technologies, 2022
Jayant Giri, Shreya Dhapke, Dhananjay Mutyarapwar
Technology has changed a lot, and there is a growing need for fast and accurate systems to fulfill the needs of people. In today’s world, there is the availability of large volumes of data in every domain. This has been possible due to the development of various hardware systems with vast storage capacity to store such a big amount of data. Due to these reasons Machine Learning (ML) and Artificial Intelligence (AI) emerged as the recent trends in technology. Mechanical ventilation is a procedure often implemented on patients with respiratory failure. It is a core therapy that is provided to the intensive care unit (ICU) patients suffering from critical illness. A ventilator delivers an air and oxygen mixture, with elevated oxygen content, to a patient’s respiratory system through an endotracheal tube to facilitate the adequate exchange of oxygen and carbon dioxide, which reduces the patient’s effort to breath and prevents the alveoli from collapsing. However, to use a mechanical ventilator, one needs to be aware of the modes and several control parameters of the ventilator. These are controls are managed by highly-trained medical professionals, who are specialized in the care of respiratory illnesses, the Respiratory Therapists. These therapists are essential for the appropriate care of mechanically ventilated patients. But the conventional way of manual monitoring of mechanical ventilators utilizes more time, human effort, and is not cost-effective.
Application of 3D Printing in COVID-19
Published in Salah-ddine Krit, Vrijendra Singh, Mohamed Elhoseny, Yashbir Singh, Artificial Intelligence Applications in a Pandemic, 2022
M. Anantha Sunil, T. Sanjana, Akshata Rai, Apoorva G. Kanthi
The main precautions that could be taken to prevent the spread of COVID-19 is to wear a mask when outdoors, use of gloves, keeping track of immunity, and maintain social distancing. The need for things like masks, gloves, face shields is increasing by the day. If a person does not have a strong immune system or comes in contact with someone suffering from COVID-19, that person can become infected as well. The treatment after contracting COVID-19 requires ventilators for patients who have acute illness. The science and medical industries yet again have come together, with the help of additive manufacturing, to deal with this problem. The application of 3D printing in COVID-19 is segregated into six major categories: Emergency wards, visualization and practice aids, testing devices, personal protective equipment (PPE), medical devices, and medically complimenting devices such as mask adjusters. You will read about these domains in the following sections.
Artificial Intelligence-enabled Automated Medical Prediction and Diagnosis in Trauma Patients
Published in Richard Jiang, Li Zhang, Hua-Liang Wei, Danny Crookes, Paul Chazot, Recent Advances in AI-enabled Automated Medical Diagnosis, 2022
Lianyong Li, Changqing Zhong, Gang Wang, Wei Wu, Yuzhu Guo, Zheng Zhang, Bo Yang, Xiaotong Lou, Ke Li, Fleming Yang
Patients with severe traumatic brain injury are prone to disturbances of consciousness. These patients often need mechanical ventilation to protect the airway and assist in breathing. However, the ventilator can cause lung injury, ventilator-related pneumonia and other related complications, with some patients failing to wean off the ventilator completely. It is also an important task to accurately predict when to wean off the ventilator or determine whether the patients with craniocerebral trauma need to extend mechanical ventilation. Abujaber et al. [1] used Logistic Regression (LR), Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), and C.5 Decision Tree (C.5 DT) and other methods to analyze the TBI patients who received mechanical ventilation as well as the severity of the injury to the head area. The prediction model was determined. The results showed that the machine learning method was superior to the traditional multivariate analysis method in predicting mechanical ventilation of traumatic brain injury. The machine learning model showed a higher prediction success rate and discriminative ability and more stable performance.
Coalitional strategies of the pharmaceutical supply chain with an option contract to cope with disruption risks
Published in International Journal of Logistics Research and Applications, 2022
Xinhua He, Wenjun Liu, Wenfa Hu, Xianhua Wu
There is a lot of literature on PSC disruption following recent disasters. Scholars are fascinated in studying how to fight against PSC disruptions during a pandemic. According to Guerin, Singh, and Strub (2020), the Indian pharmaceutical industry is vital to meet the needs of the domestic market and to maintain the stability of the global market, and they suggest that prevention of the detrimental disruption in the Indian PSC and preparation for the mass pharmaceutical production could help to cope with epidemics. According to Iyengar et al. (2020), the global health care system is facing severe challenges in obtaining ventilators to cure patients infected with the COVID-19 and it is imminent to strengthen the ventilator supply chain and produce more ventilators. After investigating the interaction between 3D printing technology and PSC development, Attaran (2020) found that PSC fragility caused by the COVID-19 promoted the development of 3D printing, which reciprocally secured that pharmaceutical product, e.g. masks, ventilators, and nasal sampling swabs, were produced timely on-site. Taking into account the surge in confirmed cases of the COVID-19 and the demand for global pharmaceutical products, Shokrani et al. (2020) called for pharmaceutical companies to manufacture products by alternative materials in distributed places to meet the pharmaceutical demands.
Influence of nanotechnology in polymeric textiles, applications, and fight against COVID-19
Published in The Journal of The Textile Institute, 2021
A medical ventilator as shown in Figure 12c is medical equipment consisting of a textile ventilator bag constructed using high-density polyethylene (HDPE) fibers. During COVID-19 attack, ventilators are utilized in facilitating patients’ respiration (World Health Organisation, 2020). The medical ventilator improves or replaces the patient’s muscular breathing relative to inhalation and exhalation of gases to and from the lungs. Disposable bed sheets (DBS) are useful when extending medication to COVID-19 infected patients designed both comfort provision, along with infection control, assisting in minimizing cross infection risks emanating from linen washing (Mottrie, 2020). Hydrophobic PP material is also utilized in constructing DBS because of inherent softness, comfortability, and stain inhibition. Figure 12d shows DBS. DBS are characterized by hypoallergenic, chemically inert to dyes and bleach, very soft, eco-friendly, possessing pilling resistance, biodegradability, compostability, skin friendly, comfortability, ligh-weight, stain resistance and so on, good for patients with mild or heavy incontinence (Royal College of Surgeons of England, 2020).
Advanced mechanical ventilation modes: design and computer simulations
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2021
Although the mechanical ventilator’s working principle can be based on negative or positive pressure, negative pressure ventilators are not used much nowadays (Bayram and Şancı 2019). On the other hand, since the clinical scenarios of diseases and the treatment strategies generally differ from patient to patient, there exists various mechanical ventilator (basic and more advanced) modes to be used (Shi et al. 2017). However, in the clinical practice, the conventional modes such as pressure controlled ventilation (PCV), volume controlled ventilation (VCV), pressure support ventilation (PSV) are still mostly used ventilation modes due to the slow presentation and the complex structure/configuration of the advanced modes (Serna et al. 2010; Suarez-Sipmann 2014). It is possible to say that this situation also exists in the simulation studies of mechanical ventilation modes. Although there are several studies for the usage/simulation of basic mechanical ventilation modes (Serna et al. 2010; Shi et al. 2014, 2019; Albanese et al. 2016; Shen et al. 2018; Hao et al. 2019), the consideration of the advanced modes is limited in the literature (Tehrani 2013; Van der Staay and Chatburn 2018).