Internet of Things and Robotics in Transforming Current-Day Healthcare Services

Strategies for preventing anterior cruciate ligament injuries in athletes: Insights from a scoping review

Background and aims

Anterior cruciate ligament (ACL) injuries significantly impact young athletes, leading to long-term physical, psychological, and socioeconomic consequences. There is an urgent need to develop effective preventive strategies. This scoping review aims to evaluate modifiable and non-modifiable risk factors for ACL injuries and assess existing preventive strategies.

Methods

A comprehensive literature search was performed on November 20, 2024, across databases including Embase, PubMed, Web of Science, Google Scholar, and Cochrane Library. Articles published between 2000 and 2024 focusing on ACL injury prevention were included, while editorial content, non-clinical studies, and non-English publications were excluded.

Results

The search yielded 322 articles, narrowing down to 94 after deduplication. After title screening, 46 studies progressed to full review, ultimately leading to the inclusion of 14 relevant manuscripts. Key findings indicate that ACL injuries result from various factors, with female athletes being 2.2 times more susceptible. Genetic predispositions linked to collagen-related SNPs and biomechanical patterns, such as landing techniques and knee positioning, were identified as significant contributors. Effective preventive programs integrated strength training, neuromuscular training, and advanced technology, including wearable devices and video analysis.

Conclusion

The study reinforces the importance of a personalized, multifaceted approach to ACL injury prevention, focusing on risk stratification and optimizing training strategies alongside technological advancements. Implementing these strategies can significantly mitigate the risk of ACL injuries in high-risk populations.

Polyhydroxybutyrate as a Novel Biopolymer for Dental Restorative Materials: Biological and Morphological Analysis

Polyhydroxybutyrate (PHB) is a biopolymer produced by bacteria. This study aimed to implement the production process of experimental medical-grade PHB and to evaluate its morphology and biocompatibility compared to conventional resin-based composites (RBCs). PHB raw material was produced via biological process and then the membrane was generated via electrospinning specifically for this study and imaged with Micro-Computed Tomography (Micro-CT) and scanning electron microscopy (SEM). MTS assay was used to assess the cytotoxicity of PHB compared to other materials. Test groups included two packable resin composites (Point 4-Kerr, G-aenial anterior-GC), two flowable resin composites (Filtek Ultimate Flowable-3M ESPE, Nova Compo HF-Imicryl), a compomer (Nova Compomer-Imicryl), a fissure-sealant (Fissured Nova Plus-Imicryl), and the PHB membrane (Innovaplast Biotechnology Inc., Eskisehir, Turkey). A control group consisting of cells without any test material was also produced. To perform the MTS assay, disc-shaped specimens of the aforementioned materials were prepared and then incubated with mouse fibroblast cells (L929) for 24 and 48 h. Micro-CT and SEM images revealed a homogeneous and fibrillary structure of the PHB. MTS assay revealed the highest cell viability in the PHB, Nova Compomer, and Fissured Nova Plus groups after 24 h. PHB and Nova Compomer showed the highest viability rates at 48 h while other RBCs had rates below 25% (p < 0.05). Considering the cell viability data and its fibrillary structure, from a biological point of view, PHB seems a promising biopolymer that might have applications in the field of dental restorative materials.

Implementing Autonomous Control in the Digital-Twins-Based Internet of Robotic Things for Remote Patient Monitoring

Conventional patient monitoring methods require skin-to-skin contact, continuous observation, and long working shifts, causing physical and mental stress for medical professionals. Remote patient monitoring (RPM) assists healthcare workers in monitoring patients distantly using various wearable sensors, reducing stress and infection risk. RPM can be enabled by using the Digital Twins (DTs)-based Internet of Robotic Things (IoRT) that merges robotics with the Internet of Things (IoT) and creates a virtual twin (VT) that acquires sensor data from the physical twin (PT) during operation to reflect its behavior. However, manual navigation of PT causes cognitive fatigue for the operator, affecting trust dynamics, satisfaction, and task performance. Also, operating manual systems requires proper training and long-term experience. This research implements autonomous control in the DTs-based IoRT to remotely monitor patients with chronic or contagious diseases. This work extends our previous paper that required the user to manually operate the PT using its VT to collect patient data for medical inspection. The proposed decision-making algorithm enables the PT to autonomously navigate towards the patient's room, collect and transmit health data, and return to the base station while avoiding various obstacles. Rather than manually navigating, the medical personnel direct the PT to a specific target position using the Menu buttons. The medical staff can monitor the PT and the received sensor information in the pre-built virtual environment (VE). Based on the operator's preference, manual control of the PT is also achievable. The experimental outcomes and comparative analysis verify the efficiency of the proposed system.

A scoping review of the use of intelligent assistive technologies in rehabilitation practice with older adults

PURPOSE

There is growing interest in intelligent assistive technologies (IATs) in the rehabilitation and support of older adults, however, the factors contributing to or preventing their use in practice are not well understood. This study aimed to develop an overview of current knowledge on barriers and facilitators to the use of smart technologies in rehabilitative practice with older adults.

MATERIALS AND METHODS

We undertook a scoping review following guidelines proposed by Arksey and O'Malley (2005) and Levac et al. (2010). A computerised literature search was conducted using the Scopus and Ovid databases, yielding 7995 citations. Of these, 94 studies met inclusion criteria. Analysis of extracted data identified themes which were explored in semi-structured interviews with a purposefully selected sample of seven clinical rehabilitation practitioners (three physical therapists, two occupational therapists, and two speech-language pathologists).

RESULTS

Barriers and facilitators to using these technologies were associated with accessibility, reported effectiveness, usability, patient-centred considerations, and staff considerations.

CONCLUSIONS

Collaborative efforts of policy-makers, researchers, manufacturers, rehabilitation professionals, and older persons are needed to improve the design of technologies, develop appropriate funding and reimbursement strategies, and minimise barriers to their appropriate use to support independence and quality of life. Any strategies to improve upon barriers to prescribing smart technologies for older people should leverage the expertise of rehabilitation professionals operating at the interface between older people; their health/mobility; their families; and technology-based solutions.

Artificial Intelligence With Robotics in Healthcare: A Narrative Review of Its Viability in India

This short review focuses on the emerging role of artificial intelligence (AI) with robotics in the healthcare sector. It may have particular utility for India, which has limited access to healthcare providers for a large growing population and limited health resources in rural India. AI works with an amalgamation of enormous amounts of data using fast and complex algorithms. This permits the software to quickly adapt the pattern of the data characteristics. It has the possibility to collide with most of the facets of the health system which may range from discovery to prediction and deterrence. The use of AI with robotics in the healthcare sector has shown a remarkable rising trend in the past few years. Functions like assistance with surgery, streamlining hospital logistics, and conducting routine checkups are some of the tasks that may be managed with great efficiency using artificial intelligence in urban and rural hospitals across the country. AI in the healthcare sector is advantageous in terms of ensuring exclusive patient care, safe working conditions where healthcare providers are at a lower risk of getting infected, and perfectly organized operational tasks. As the healthcare segment is globally recognized as one of the most dynamic and biggest industries, it tends to expedite development through modernization and original approaches. The future of this lucrative industry is looking forward to a great revolution aiming to create intelligent machines that work and respond like human beings. The future perspective of AI and robotics in the healthcare sector encompasses the care of elderly people, drug discovery, diagnosis of deadly diseases, a boost in clinical trials, remote patient monitoring, prediction of epidemic outbreaks, etc. However, the viability of using robotics in healthcare may be questionable in terms of expenditure, skilled workforce, and the conventional mindset of people. The biggest challenge is the replication of these technologies to the smaller towns and rural areas so that these facilities may reach the larger segment of the entire population of the country. This review aims to examine the adaptability and viability of these new technologies in the Indian scenario and identify the major challenges.

Sensor technology usage in orthopedic trauma

Medicine in general is quickly transitioning to a digital presence. Orthopaedic surgery is also being impacted by the tenets of digital health but there are also direct efforts with trauma surgery. Sensors are the pen and paper of the next wave of data acquisition. Orthopaedic trauma can and will be part of this new wave of medicine. Early sensor products that are now coming to market, or are in early development, will directly change the way we think about surgical diagnosis and outcomes. Sensor development for biometrics is already here. Wellness devices, pressure, temperature, and other parameters are already being measured. Data acquisition and analysis is going to be a fruitful addition to our research armamentarium with the volume of information now available. A combination of broadband internet, micro electrical machine systems (MEMS), and new wireless communication standards is driving this new wave of medicine. The Internet of Things (IoT) [1] now has a subset which is the Internet of Medical Devices [2-5] permitting a much more in-depth dive into patient procedures and outcomes. IoT devices are now being used to enable remote health monitoring, in hospital treatment, and guide therapies. This article reviews current sensor technology that looks to impact trauma care.

Providing Care: Intrinsic Human-Machine Teams and Data

Despite the many successes of artificial intelligence in healthcare applications where human-machine teaming is an intrinsic characteristic of the environment, there is little work that proposes methods for adapting quantitative health data-features with human expertise insights. A method for incorporating qualitative expert perspectives in machine learning training data is proposed. The method implements an entropy-based consensus construct that minimizes the challenges of qualitative-scale data such that they can be combined with quantitative measures in a critical clinical event (CCE) vector. Specifically, the CCE vector minimizes the effects where (a) the sample size is too small, (b) the data may not be normally distributed, or (c) The data are from Likert scales, which are ordinal, so parametric statistics cannot be used. The incorporation of human perspectives in machine learning training data provides encoding of human considerations in the subsequent machine learning model. This encoding provides a basis for increasing explainability, understandability, and ultimately trust in AI-based clinical decision support system (CDSS), thereby improving human-machine teaming concerns. A discussion of applying the CCE vector in a CDSS regime and implications for machine learning are also presented.

Sensors and digital medicine in orthopaedic surgery

Digital health principles are starting to be evident in medicine. Orthopaedic trauma surgery is also being impacted —indirectly by all other improvements in the health ecosystem but also in particular efforts aimed at trauma surgery. Data acquisition is changing how evidence is gathered and utilized. Sensors are the pen and paper of the next wave of data acquisition. Sensors are gathering wide arrays of information to facilitate digital health relevance and adoption. Early adaption of sensor technology by the nonlegacy health environment is what has made sensor driven data acquisition so palatable to the normal health care system. As it applies to orthopaedic trauma, current sensor driven diagnostics and surveillance are nowhere near as developed as in the larger medical community. Digital health is being explored for health care records, data acquisition in diagnostics and rehabilitation, wellness to health care translation, intraoperative monitoring, surgical technique improvement, as well as some early-stage projects in long-term monitoring with implantable devices. The internet of things is the next digital wave that will undoubtedly affect medicine and orthopaedics. Internet of things (loT) devices are now being used to enable remote health monitoring and emergency notification systems. This article reviews current and future concepts in digital health that will impact trauma care.

Potential of Internet of Medical Things (IoMT) applications in building a smart healthcare system: A systematic review

Sudden spurting of Corona virus disease (COVID-19) has put the whole healthcare system on high alert. Internet of Medical Things (IoMT) has eased the situation to a great extent, also COVID-19 has motivated scientists to make new 'Smart' healthcare system focusing towards early diagnosis, prevention of spread, education and treatment and facilitate living in the new normal. This review aims to identify the role of IoMT applications in improving healthcare system and to analyze the status of research demonstrating effectiveness of IoMT benefits to the patient and healthcare system along with a brief insight into technologies supplementing IoMT and challenges faced in developing a smart healthcare system. An internet-based search in PUBMED, Google Scholar and IEEE Library for english language publications using relevant terms resulted in 987 articles. After screening title, abstract, and content related to IoMT in healthcare and excluding duplicate articles, 135 articles published in journal with impact factor ≥1 were eligible for inclusion. Also relevant articles from the references of the selected articles were considered. The habituation of IoMT and related technology has resolved several difficulties using remote monitoring, telemedicine, robotics, sensors etc. However mass adoption seems challenging due to factors like privacy and security of data, management of large amount of data, scalability and upgradation etc. Although ample knowledge has been compiled and exchanged, this structured systematic review will help the healthcare practitioners, policymakers/decision makers, scientists and researchers to gauge the applicability of IoMT in healthcare more efficiently.