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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.

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.

Accurately identifying needle tip and seed positions for low dose rate prostate brachytherapy on MRI images is challenging. Uncertainties in locating needle tip positions can lead to misplacement of seeds compared to planned coordinates. Furthermore uncertainty in establishing true seed positions on the images, leads to uncertainty in the dose distributions.

In this study, a novel phantom has been designed for the analysis of I-125 seed and needle tip detection and tip image distortion. The phantom utilises a gel that mimics prostate tissue in MRI, to evaluate the uncertainty in establishing seed and needle tip positions. Reults are reported for the IsoSeed (Bebig) source, in clinically relevant seed arrangements, and for a novel nitinol needle.

The choice of MRI sequence impacts the accuracy of detecting the needle tips and seeds. This is most prevelant when the seeds are in clusters, at the boundary of the prostate and at 90˚ to the long axis of the scanner. Detected needle tip position, when the MRI metal artefact correction algorithm was used, was measured consistently inferior to the actual position (mean tip at -2.3 ± 1.5 mm (k = 2), p = 0.03).

We have demonstrated the design of a phantom that can be used to quantitatively assess seed and needle tip positions simultaneously, to establish the accuracy of detection, or presence of artefacts on MRI.

This paper reports learning points from a small pilot study using a portable diagnostic X-ray set to radiograph patients in novel settings such as the patient home or care home. This paper explores issues associated with regulatory compliance, staff training, oversight of radiation safety and the drafting of key risk safety documentation including risk assessments. Some limitations to diagnostic imaging are explored and a simple subjective assessment of the visual clarity presented. The pilot demonstrated potential for starting treatment sooner without recourse to a hospital visit. It was well received by patients and all images were of diagnostic quality but was more labour intensive compared with traditional methods. Likely barriers and potential advantages to implementing a full clinical service are discussed.

A bespoke phantom has been designed, with clinically relevant features for endoanal ultrasound (EAUS), capable of rigorously assessing the performance of 360${}^{\circ }$ ultrasound probes.

The performance of three, commercially available, anorectal probes, capable of producing both 2D and 3D images, was assessed. One of the probes was also assessed in two states: before failure and after a repair to correct a failure. For each probe the signal to noise ratio (SNR), contrast to noise ratio (CNR), penetration depth, resolution, focus depth, distance accuracy and low contrast object detectability (LCOD) were assessed at varying dynamic ranges, receive gains and operating frequencies. A Python program (SAUQA) was developed to semi-automate the analysis.

In general the measured parameters varied as expected. However, at intermediate receive gains, adjusting the receive gain resulted in the SNR, CNR, penetration depth and LCOD varying in an unexpected manner. The reason for this is not known, but because it was exhibited by all probes it is believed to be related to the ultrasound machine itself and/or an inherent characteristic of the probe design.

The quantitative results suggest that all probes tested offer an effective method of assessing the integrity of the Internal Anal Sphincter (IAS) and the repair of the probe appears to have been successful. However, differences between the probes were observed both quantitatively and qualitatively, with the original probe providing the best results for EAUS.

In light of the results, a recommendation was made, to the EAUS service at University Hospitals Dorset, to adjust the default machine start-up settings for EAUS.

In the UK, the Institute of Physics and Engineering in Medicine (IPEM) provides guidance to Medical Physics departments on appropriate Quality Control (QC) tests to evaluate MRI scanners used in routine clinical practice. The method recommended for the rigorous annual assessment of the SNR produced by RF coils uses a sequence and regions of interest (ROIs) recommended by IPEM, and calculates SNR through a subtraction calculation (IPEM recommended Method). This method was compared to alternative methods proposed by NessAiver at the 2019 American Association of Physicists in Medicine meeting in their talk on RF coil testing. Comparisons were completed for sequences and regions of interest (ROIs) recommended by IPEM and NessAiver. Testing was performed at 1.5 T using the scanner’s integrated body coil and at 3.0 T using a peripheral Head/Neck coil. Calculation of SNR using the mean of the background noise, assessed using the NessAiver recommended sequence and ROIs (NessAiver Noise-Average Method), typically offered the lowest variability in SNR results. Additionally, the SNR results produced by the IPEM Recommended Method were less repeatable than those from the NessAiver Noise-Average Method (p$<$0.001). Furthermore, the significance level to which a simulated reduction in SNR could be detected using the IPEM Recommended Method (p$<$0.001) was less than with the NessAiver Noise-Average Method (p$\ge$0.0031). Finally, when comparing the test duration of each method, use of the NessAiver Noise-Average Method results in a 90% reduction in acquisition time per SNR result, when compared to the IPEM Recommended Method.

Objectives

To develop a standardised template to support physiotherapist reporting of lower limb kinematic waveform data

Design

Within and between user agreement identification of movement compensation strategies.

Setting

University Health Board Physiotherapy Department

Participants

Fourteen individuals with anterior cruciate ligament reconstruction performed overground gait, double-leg squat, and stair ascent wearing body-worn sensors. Six users viewed 252 kinematic waveforms of hip, knee and ankle joint angles in the sagittal and frontal planes.

Main outcome measures

Between and within-user observed agreement and themes from movement analysis reports

Results

Between-user observed agreement for presence of a movement compensation was 0.6–0.9 for the sagittal plane and 0.75–1.0 for the frontal place. Within-user observed agreement was 0.57–1.00 for the sagittal plane and 0.71–1.00 for the frontal plane. Three themes and seven categories were identified from the waveform interpretations: Amount (qualitative and quantitative description), timing (phase, discrete time point, cycle), and nature (peak, range of motion, timing) of the compensation.

Conclusion

There was good agreement between users at identifying the presence of movement compensation from the kinematic waveforms, but there was variation in how movement compensations were described. An interactive report, a standardised template for interpretation of kinematic waveforms, and training to support the clinical application of a movement analysis toolkit are proposed.

Artificial intelligence and machine learning applications are increasingly prevalent in the healthcare industry. In some cases, medical devices use sensor-collected data to feed into algorithms which generate scores or risk assessments that are used to inform patient care. The process of determining sensor accuracy requirements which will ensure that the algorithm generates reliable scores is not straightforward or well-defined. In this paper, we describe a simulation-based method to characterize sensor accuracy requirements for a device that uses a machine-learning algorithm to generate a postural stability score – the ZIBRIO Stability Scale. The results of the simulation are described, as is the application to sensor selection in preparation for manufacturing of the device. Other medical device developers may be able to use this method or similar methods in their requirements engineering process.

Ultrasound analysis is an instantaneous characterization tool to evaluate microstructural inhomogeneity. In this study, computational high-frequency ultrasound analysis was conducted to characterize histological features of malignant breast tissue. A high-frequency ultrasound signal was sent through the soft tissue model in a through-transmission manner. Histological features of the soft tissue were categorized as cell shape, nuclear pleomorphism, and malignant cell density. The design of experiment was created by combining various levels of histological features of tumor tissue. Transmitted ultrasound frequency spectrums from all combinations of histological features were analyzed in terms of peak density and mean peak to valley distance (MPVD) parameters. For the circular-shaped cell model, peak density and MPVD responded with increasing and decreasing trends respectively while the malignant histological features became gradually dominant. For the elliptical-shaped cell model, only peak density was effective to establish a relationship with the histological features. It was observed that added malignant cells had more contribution to the response parameters than nuclear pleomorphism. Furthermore, the frequency spectrum patterns from all histological combinations were evaluated to find further information about malignant features.