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It has been recognized that that the design and prescription of Energy Storing and Returning prosthetic running feet are not well understood and that further information on their performance would be beneficial to increase this understanding. Dynamic analysis of an amputee wearing a prosthetic foot is typically performed using reflective markers and motion-capture systems. High-speed cameras and force plates are used to collect data of a few strides. This requires specialized and expensive equipment in an unrepresentative environment within a large area. Inertial Measurement Units are also capable of being used as wearable sensors but suffer from drift issues. This paper presents the development of a wearable sensing system that records the action of an Energy Storing and Returning prosthetic running foot (sagittal plane displacement and ground contact position) which could have research and/or clinical applications. This is achieved using five standalone pieces of apparatus including foot-mounted pressure sensors and a rotary vario-resistive displacement transducer. It is demonstrated, through the collection of profiles for both foot deflection and ground contact point over the duration of a stride, that the system can be attached to an amputee’s prosthesis and used in a non-laboratory environment. It was found from the system that the prosthetic ground contact point, for the amputee tested, progresses along the effective metatarsal portion of the prosthetic foot towards the distal end of the prosthesis over the duration of the stride. Further investigation of the effective stiffness changes of the foot due to the progression of the contact point is warranted.

The medical imaging community has embraced Machine Learning (ML) as evidenced by the rapid increase in the number of ML models being developed, but validating and deploying these models in the clinic remains a challenge. The engineering involved in integrating and assessing the efficacy of ML models within the clinical workflow is complex. This paper presents a general-purpose, end-to-end, clinically integrated ML model deployment and validation system implemented at UCSF. Engineering and usability challenges and results from 3 use cases are presented. A generalized validation system based on free, open-source software (OSS) was implemented, connecting clinical imaging modalities, the Picture Archiving and Communication System (PACS), and an ML inference server. ML pipelines were implemented in NVIDIAs Clara Deploy framework with results and clinician feedback stored in a customized XNAT instance, separate from the clinical record but linked from within PACS. Prospective clinical validation studies of 3 ML models were conducted, with data routed from multiple clinical imaging modalities and PACS. Completed validation studies provided expert clinical feedback on model performance and usability, plus system reliability and performance metrics. Clinical validation of ML models entails assessing model performance, impact on clinical infrastructure, robustness, and usability. Study results must be easily accessible to participating clinicians but remain outside the clinical record. Building a system that generalizes and scales across multiple ML models takes the concerted effort of software engineers, clinicians, data scientists, and system administrators, and benefits from the use of modular OSS. The present work provides a template for institutions looking to translate and clinically validate ML models in the clinic, together with required resources and expected challenges.

Abstract Background Identification of causes of dementia soon after symptom onset is important, because appropriate treatment of some causes of dementia can slow or halt its progression or enable symptomatic treatment where appropriate. The accuracy of MRI and CT, and whether MRI is superior to CT, in detecting a vascular component to dementia in autopsy confirmed and clinical cohorts of patients with VaD, combined AD and VaD (“mixed dementia”), and AD remain unclear. We conducted a systematic review and meta-analysis to investigate this question. Methods We searched eight databases and screened reference lists to identify studies addressing the review question. We assessed study quality using QUADAS. We estimated summary diagnostic accuracy according to imaging finding, and ratios of diagnostic odds ratios (RDORs) for MRI versus CT and high versus low risk of bias. Results We included 7 autopsy and 31 non-autopsy studies. There was little evidence that selective patient enrolment and risk of incorporation bias impacted on diagnostic accuracy (p = 0.12 to 0.95). The most widely reported imaging finding was white matter hyperintensities. For CT (11 studies) summary sensitivity and specificity were 71% (95% CI 53%-85%) and 55% (44%-66%). Corresponding figures for MRI (6 studies) were 95% (87%-98%) and 26% (12%-50%). General infarcts was the most specific imaging finding on MRI (96%; 95% CI 94%-97%) and CT (96%; 93%-98%). However, sensitivity was low for both MRI (53%; 36%-70%) and CT (52%; 22% to 80%). No imaging finding had consistently high sensitivity. Based on non-autopsy studies, MRI was more accurate than CT for six of seven imaging findings, but confidence intervals were wide. Conclusion There is insufficient evidence to suggest that MRI is superior to CT with respect to identifying cerebrovascular changes in autopsy-confirmed and clinical cohorts of VaD, AD, and ‘mixed dementia’.

Adapting ways to functionalize polymer materials is becoming increasingly important to their implementation in translational biomedical sciences. By tuning the mechanical, chemical, and biological qualities of these materials, their applications can be broadened, opening the door for more advanced integration into modern medical techniques. Here, we report on a method to integrate chemical functionalizations into discrete, microscale polymer structures, which are used for tissue engineering applications, for in vivo localization, and three-dimensional manipulation. Iron oxide nanoparticles were incorporated into the polymer matrix using common photolithographic techniques to create stably functional microstructures with magnetic potential. Using magnetic resonance imaging (MRI), we can promote visualization of microstructures contained in small collections, as well as facilitate the manipulation and alignment of microtopographical cues in a realistic tissue environment. Using similar polymer functionalization techniques, fluorine-containing compounds were also embedded in the polymer matrix of photolithographically fabricated microstructures. The incorporation of fluorine-containing compounds enabled highly sensitive and specific detection of microstructures in physiologic settings using fluorine MRI techniques ((19)F MRI). These functionalization strategies will facilitate more reliable noninvasive tracking and characterization of microstructured polymer implants as well as have implications for remote microstructural scaffolding alignment for three-dimensional tissue engineering applications.

Purpose: To clarify the causal relationship between factors contributing to the postoperative survival of patients with esophageal cancer. Methods: A cohort of 195 patients who underwent surgery for esophageal cancer between 2008 and 2021 was used in the study. All patients had preoperative chest computed tomography (CT) and positron emission tomography‐CT (PET‐CT) scans prior to receiving any treatment. From these images, high throughput and quantitative radiomic features, tumor features, and various body composition features were automatically extracted. Causal relationships among these image features, patient demographics, and other clinicopathological variables were analyzed and visualized using a novel score‐based directed graph called "Grouped Greedy Equivalence Search" (GGES) while taking prior knowledge into consideration. After supplementing and screening the causal variables, the intervention do‐calculus adjustment (IDA) scores were calculated to determine the degree of impact of each variable on survival. Based on this IDA score, a GGES prediction formula was generated. Ten‐fold cross‐validation was used to assess the performance of the models. The prediction results were evaluated using the R‐Squared Score (R2 score). Results: The final causal graphical model was formed by two PET‐based image variables, ten body composition variables, four pathological variables, four demographic variables, two tumor variables, and one radiological variable (Percentile 10). Intramuscular fat mass was found to have the most impact on overall survival month. Percentile 10 and overall TNM (T: tumor, N: nodes, M: metastasis) stage were identified as direct causes of overall survival (month). The GGES casual model outperformed GES in regression prediction (R2 = 0.251) (p < 0.05) and was able to avoid unreasonable causality that may contradict common sense. Conclusion: The GGES causal model can provide a reliable and straightforward representation of the intricate causal relationships among the variables that impact the postoperative survival of patients with esophageal cancer. [ABSTRACT FROM AUTHOR]

Assignments were altered to determine if interspersing additional briefer and easier mathematics problems enhanced students' accuracy on longer, more difficult target problems across two types of mathematics assignments. Students (N = 52) from three fifth-grade classes completed six math assignments incorporating two task demands and three interspersal ratios. The interspersal ratios were no interspersal, one interspersed problem per three target problems (1:3), and one interspersal problem per one target problem (1:1). For the written (low-attention) assignments, students completed problems via paper and pencil. For the cognitive (high-attention) assignments, the experimenter read problems aloud and students had to compute mathematics problems in their heads, using paper and pencil only to record answers. Results showed that for the cognitive assignments, target problem accuracy was significantly higher for the 1:3 ratio relative to the other two ratios. On the written assignments, target problem accuracy levels were significantly higher on the 1:1 ratio assignments relative to the no interspersal assignments. Discussion focuses on applied and theoretical implications of the current results. (Contains 3 tables.)