IPEM-translation最新文献

AI segmentation has been recently introduced in the local department for delineation of targets and organs-at-risk (OAR) for a wide range of tumour sites. For breast radiotherapy, AI segmentation can provide target delineation (breast and lymph nodes) and required OAR, and this has enabled a stepwise series of improvements to the local planning technique.

Clinician feedback deemed 67 - 89 % of nodal target volumes required no edits or only minor edits, so AI breast and lymph nodes volumes were first used to guide tangent and supraclavicular field placement, instead of a bony-anatomy based technique.

Next, evolution from anatomical field-placement to true inverse optimised planning was introduced using AI to create the required target volumes. For internal mammary node (IMN) treatments, the previous 3-field technique prohibited Deep Inspiration breath-hold (DIBH), due to the couch rotation used to match field edges. The roll-out of VMAT (volumetric-modulated arc therapy) with DIBH enabled by AI therefore resulted in a dose reduction to ipsi-lateral lung, and in mean heart dose compared to the old 3-field technique. Median time from CT scan to VMAT IMN plan approval reduced from 12 days (with manual contouring) to 7 days using reviewed and edited AI-generated volumes.

Consistent, high-quality contours for 9 OAR and breast PTVs for all patients facilitates comparison with NHS-E scorecards as a benchmark for plan quality. Workflows have been simplified, with significant time-savings. DIBH radiotherapy is now available to more patients, further improving dose sparing for heart and lung.

Starting from the recently published idea of the “perfect machine”, we argue that quantitative MRI (qMRI) supported by a rigorous metrological framework could not only drastically improve reproducibility in MRI and support large-scale studies, there is also scope to accredit individual MRI scanners in particular applications via a suitable accreditation scheme. We present the idea of the perfect diagnostic imaging machine, including describing the background of the ideas and how they lead to the idea of accreditation in qMRI. The scheme presented here is not intended to be the last word in accreditation, but to stimulate debate in the idea and whether or not is has merit for qMRI and its clinical and research context.

Climate change is increasingly a health emergency. This has been recognised by the NHS which aims to be carbon net zero by 2040. Most of the carbon footprint of radiotherapy is due to patient travel. Here we investigate if satellite centres can help reduce this impact.

The carbon footprint of construction was estimated using two different methods. The post codes for 49 patients and 21 staff were collected and the distance to the satellite centre and main centre determined. The carbon footprint from each of these aspects was combined to determine how many years it would take for the reduced patient travel to offset the construction of the satellite centre.

The mean carbon footprint of travel to the satellite centre and main centre were 116.0 kgCO₂e and 176.2 kgCO₂e respectively. The carbon footprint of building the satellite centre was between 1103 tCO₂e and 618 tCO₂e, meaning it would take 5.6 – 10.0 years to offset the embedded carbon footprint of the new building.

For the first time this study has estimated the carbon footprint of building a satellite radiotherapy centre and how this, through reducing patient travel can lower the carbon footprint of the service within a decade. This work may help those wishing to sustainably improve service provision.

Background

Manual contouring is time-consuming and subjective. Thus, auto-segmentation methods, which can be deployed in the existing workflow, are needed. The objective of this study was to assess the feasibility of Limbus AI and AI Rad Companion auto-contours for head and neck treatment planning.

Methods

Head and neck patients treated with RapidArc were selected retrospectively. The manual contours on the planning CT were used as reference. Geometric analysis of the auto-contours was performed using several evaluation metrics such as the Dice Similarity Coefficient (DSC) and the Mean Distance to Conformity (MDC). Dosimetric analysis was performed by recalculating the original plan on the auto-contours and comparing Dose Volume Histogram (DVH) metrics to the original plan.

Results and discussion

Both AI tools tend to underestimate the volumes of brainstem and cord. For brainstem and parotids, median DSC values were ≥ 0.8. For all auto-contours, median MDC values were ∼ 3–6 mm. Median differences were found of up to ±7 % in DVH points on the auto-contours relative to the planning CT contours, but these were not statistically-significant.

Conclusion

The auto-contours could be used as a starting point to assist the clinician with the manual contouring of structures on the planning and re-scanning planning CT.

Objectives

There is an urgent need for technologies which can reduce the impact of airborne disease transmission. Far-UVC (200–230 nm) is a range of wavelengths growing in relevance for airborne virus disinfection in occupied public spaces. These wavelengths quickly and efficiently inactivate airborne pathogens, while to current knowledge remaining low risk to room occupants. If there is ever to be an effective widespread implementation of these technologies in public spaces, it is important to assess public opinion to ensure appropriate use and understanding of the technology.

Methods

A self-administered survey was distributed through social media channels with several questions to gather opinions on using Far-UVC. The survey was distributed between September 2021 and January 2022. Outcome measures included how safe respondents would feel with or without Far-UVC in indoor spaces and how acceptable the technology would be in certain indoor spaces.

Results

There were 111 respondents to the survey. The median age range of the respondents was 36–45, most respondents had never studied biology or related science subjects beyond school level (68%, n = 76), and 87% (n = 97) were indoor workers or attended formal education. Less than one-third of respondents had heard of the term ‘Far-UVC’. Though, on learning about the core principles of Far-UVC, respondents became more supportive of its use in public spaces. Acceptance of Far-UVC was strongest in areas where a higher benefit-risk ratio was perceived, such as in hospitals.

Conclusion

We have shown that when the basic concepts of Far-UVC are clearly communicated, public opinion on its adoption improves. Without such a general understanding amongst members of the public, Far-UVC may then face challenges in gaining widespread adoption. The assessment of public opinion presented here will help to determine where primary concerns lie, and the actions needed to address these.

Distraction osteogenesis (DO) is a classical surgical technique for limb lengthening and reconstruction (LLR). Most existing DO devices for LLR are operated manually, and the accurate DO process is user dependant, which could affect new bone formation. Recently, automated devices have been introduced for continuous DO processes to aid in tissue healing. To the best of our knowledge, few automated continuous distraction osteogenesis (ACDO) devices have focused on DO surgery for the long bones of the extremities and monitoring of their status during the surgical process. This study presents a novel ACDO device, which is driven by a deceleration stepper motor for further reduction in total mass and amplification in distraction force, including a precise and programmable man–machine-interaction system to allow surgeons to control and monitor the treatment remotely. The mechanical device was verified to be capable of generating a continuous and controllable distraction force and rate. The proposed man–machine-interaction system possesses the functions of customizing and following up on treatment plan by clinicians, including setting the DO process, measuring and displaying parameters, and uploading DO information to the data cloud. During electromechanical system simulation and prototype experiments, the performance of the proposed system was consistent with the setting DO parameters and treatment plan.

The infectious disease known as COVID-19 has spread dramatically all over the world since December 2019. The fast diagnosis and isolation of infected patients are key factors in slowing down the spread of this virus and better management of the pandemic. Although the CT and X-ray modalities are commonly used for the diagnosis of COVID-19, identifying COVID-19 patients from medical images is a time-consuming and error-prone task. Artificial intelligence has shown to have great potential to speed up and optimize the prognosis and diagnosis process of COVID-19. Herein, we review publications on the application of deep learning (DL) techniques for diagnostics of patients with COVID-19 using CT and X-ray chest images for a period from January 2020 to October 2021. Our review focuses solely on peer-reviewed, well-documented articles. It provides a comprehensive summary of the technical details of models developed in these articles and discusses the challenges in the smart diagnosis of COVID-19 using DL techniques. Based on these challenges, it seems that the effectiveness of the developed models in clinical use needs to be further investigated. This review provides some recommendations to help researchers develop more accurate prediction models.

Telemedicine has been an essential form of care since the onset of the COVID-19 pandemic. However, telemedicine may exacerbate disparities for populations with limited digital literacy or access, such as older adults, racial minorities, patients of low income, rural residences, or limited English proficiency. From March 2020 to March 2022, this retrospective cohort study analyzed the use of in-person, phone/message, and telemedical care at a single tertiary care center in an oncology department. We investigated the association between economic, racial, ethnic, socioeconomic factors and forms of care, including in-person visits, telemedicine-based visits, and telephone/messages. The study results show that telemedicine utilization is lower among patients 65 and older, female patients, American Indian or Alaska Native patients, uninsured patients, and patients who require interpreters during clinical visits. As a result, it is unlikely that telemedicine will provide equal access to clinical care for all populations. On the other hand, in-person care utilization remains low in low-income and rural-living patients compared to the general population, while telephone and message use remains high in low-income and rural-living patients. We conclude that telemedicine is currently unable to close the utilization gap for populations of low socioeconomic status. Patients with low socioeconomic status use in-person care less frequently. For the disadvantaged, unusually high telephone or message utilization is unlikely to provide the same quality as in-person or telemedical care. Understanding the causes of disparity and promoting a solution to improve equal access to care for all patients is critical.

The high transmissibility rate of the Severe Acute Respiratory Syndrome Coronavirus 2 facilitated an exponential growth in the number of infections, posing a tremendous threat to healthcare systems across the world. The use of Non-oil 95% efficiency (N95) respirators demonstrated to reduce the risk of virus transmission. The escalated demand in N95 respirators during 2020 generated a massive shortage worldwide which resulted in serious implications, one being an increase in healthcare providers’ costs. In response, various optimization strategies were implemented. This study aimed to assess the implementation of a safe and effective re-use policy for high-efficiency filtering facepiece respirators (FFRs) in a high-complexity university hospital in 2020. Associated costs were estimated through a descriptive accounting analysis of resources saved. Acceptability, appropriateness, and feasibility rates were 80.5%, 78.8%, and 83.6%, respectively. With an implementation cost of approximately 10,000 USD, there was a 56.1% reduction in FFRs consumption, compared with a non-policy scenario, with savings exceeding 500,000 USD in 2020. In a pandemic scenario where it is vital to spare resources, a FFRs rational use policy demonstrated to be a highly cost-efficient alternative in order to save resources without increasing contagion risk among healthcare workers.

With fever being one of the most prominent symptoms of COVID-19, the implementation of fever screening has become commonplace around the world to help mitigate the spread of the virus. Non-contact methods of temperature screening, such as infrared (IR) forehead thermometers and thermal cameras, benefit by minimizing infection risk. However, the IR temperature measurements may not be reliably correlated with actual core body temperatures. This study proposed a trained model prediction using IR-measured facial feature temperatures to predict core body temperatures comparable to an FDA-approved product. The reference core body temperatures were measured by a commercially available temperature monitoring system. Optimal inputs and training models were selected by the correlation between predicted and reference core body temperature. Five regression models were tested during the study. The linear regression model showed the lowest minimum-root-mean-square error (RSME) compared with reference temperatures. The temple and nose region of interest (ROI) were identified as optimal inputs. This study suggests that IR temperature data could provide comparatively accurate core body temperature prediction for rapid mass screening of potential COVID cases using the linear regression model. Using linear regression modeling, the non-contact temperature measurement could be comparable to the SpotOn system with a mean SD of ± 0.285 °C and MAE of 0.240 °C.