Search

Abstract In total hip arthroplasty (THA), determining the center of rotation (COR) and diameter of the hip joint (acetabulum and femoral head) is essential to restore patient biomechanics. This study investigates on-the-fly determination of hip COR and size, using off-the-shelf augmented reality (AR) hardware. An AR head-mounted device (HMD) was configured with inside-out infrared tracking enabling the determination of surface coordinates using a handheld stylus. Two investigators examined 10 prosthetic femoral heads and cups, and 10 human femurs. The HMD calculated the diameter and COR through sphere fitting. Results were compared to data obtained from either verified prosthetic geometry or post-hoc CT analysis. Repeated single-observer measurements showed a mean diameter error of 0.63 mm ± 0.48 mm for the prosthetic heads and 0.54 mm ± 0.39 mm for the cups. Inter-observer comparison yielded mean diameter errors of 0.28 mm ± 0.71 mm and 1.82 mm ± 1.42 mm for the heads and cups, respectively. Cadaver testing found a mean COR error of 3.09 mm ± 1.18 mm and a diameter error of 1.10 mm ± 0.90 mm. Intra- and inter-observer reliability averaged below 2 mm. AR-based surface mapping using HMD proved accurate and reliable in determining the diameter of THA components with promise in identifying COR and diameter of osteoarthritic femoral heads.

Distorted body representations play a major role in the onset and maintenance of Schizophrenia. However, these distortions are difficult to assess because explicit assessments can provoke intense fears about the body and require a good insight. We proposed an implicit motor imagery task to a 14-year-old girl with Early-Onset Schizophrenia. The test consisted of presenting different openings varying in width. For each aperture, the young girl has to say if she could pass through without turning her shoulders. A critical aperture is determined as the first aperture for which she considered she could no longer pass, compared to her shoulders' width. The girl perceived herself as 51 % wider than she was, indicating a significant oversized body schema. The implicit assessments of body schema generate less anxiety and does not require a great level of insight; moreover, those are promising tools for early detection of disease in prodromal phases of Schizophrenia and assistance with differential diagnosis.

Artificial intelligence systems (AI-systems) are said to revolutionize healthcare. They are predicted to increase efficiency, free up time for more in-person care, lead to more accessible care, etc. However, these systems come with ethical reservations related to individuals’ privacy, dignity, social bias, etc. And although there are many approaches to the ethics of ai-systems in healthcare, most overlook these systems’ global impacts, such as their environmental and global social impacts. To meet this gap, I present some first steps to a global approach to the ethics of ai-systems in healthcare. In the first part of the presentation, I first describe some current and predicted uses of ai-systems in healthcare. Then, I shortly survey the most heavily discussed ethical issues related to these uses and present one particular common approach to meet these issues, the principlist approach. In the second part of the presentation, I argue that this common approach is unsuitable to meet the different environmental and the herewith related social impacts, such as increased health risks. Based on an interpretation of ai-systems as world-objects, which emphasizes the environmental and social materiality of ai-systems, I then present a global approach to the ethics of these systems which exists of five interrelated levels of ethical analysis and impacts: an individual-relational, organizational, societal, global, and historical level. The global approach to the ethics of ai-systems in healthcare complements current common ethical approaches, such as the principlist approach, by including ethical issues related to the environmental and social impacts, such as increased health risks, of ai-systems. Although there is already much ethical ground prepared to guide the use of ai-systems in healthcare, only by accounting for these systems’ global environmental and social impacts these systems can be ethically responsive for everyone and everywhere.

Abstract Critical limb ischemia is associated with high mortality and major amputations. Intra-arterial digital subtraction angiography (IADSA) has been the reference standard but has some shortcomings including the two-dimensional projection and the lack of tissue perfusion information. The aim of this exploratory study is to examine four-dimensional computed tomography (4DCT) angiography and perfusion imaging using low-volume intra-arterial contrast injections for an improved anatomic and hemodynamic assessment in patients with foot ulcers. Three patients underwent a low-volume (2 mL) intra-arterial contrast-enhanced 4DCT examination combined with a diagnostic IADSA. An automated assessment of blood flow and tissue perfusion from the 4DCT data was performed. Vascular structures and corresponding blood flows were successfully assessed and correlated well with the IADSA results. Perfusion values of the affected tissue were significantly higher compared to the unaffected tissue. The proposed 4DCT protocol combined with the minimal usage of contrast agent (2 mL) provides superior images compared to IADSA as three phases (arterial, perfusion, and venous) are captured. The obtained parameters could allow for an improved diagnosis of critical limb ischemia as both the proximal vasculature and the extent of the perfusion deficit in the microvasculature can be assessed. Relevance statement Intra-arterial 4DCT allows for assessing three phases (arterial, perfusion and venous) using minimal contrast (2 mL). This method could lead to an improved diagnosis of critical limb ischemia as both proximal vasculature and the extent of the perfusion deficit are assessed. Trial registration ISRCTN, ISRCTN95737449. Registered 14 March 2023—retrospectively registered, https://www.isrctn.com/ISRCTN95737449 Key points • Three phases (arterial, perfusion, and venous) are obtained from 2 mL intra-arterial 4DCT. • The obtained hemodynamic parameters correlated well with the IADSA findings. • 4DCT surpassed IADSA in terms of assessment of venous blood flow and inflammatory hyperperfusion. • The assessment of tissue perfusion could lead to optimizing the revascularization strategy. Graphical Abstract

Abstract Topochemistry enables step-by-step conversions of solid-state materials often leading to metastable structures that retain initial structural motifs. Recent advances in this field revealed many examples where relatively bulky anionic constituents were actively involved in redox reactions during (de)intercalation processes. Such reactions are often accompanied by anion-anion bond formation, which heralds possibilities to design novel structure types disparate from known precursors, in a controlled manner. Here we present the multistep conversion of layered oxychalcogenides Sr2MnO2Cu1.5 Ch 2 (Ch = S, Se) into Cu-deintercalated phases where antifluorite type [Cu1.5 Ch 2]2.5- slabs collapsed into two-dimensional arrays of chalcogen dimers. The collapse of the chalcogenide layers on deintercalation led to various stacking types of Sr2MnO2 Ch 2 slabs, which formed polychalcogenide structures unattainable by conventional high-temperature syntheses. Anion-redox topochemistry is demonstrated to be of interest not only for electrochemical applications but also as a means to design complex layered architectures.

In contrast to perfectly periodic crystals, materials with short-range order produce diffraction patterns that contain both Bragg reflections and diffuse scattering. To understand the influence of short-range order on material properties, current research focuses increasingly on the analysis of diffuse scattering. This article verifies the possibility to refine the short-range order parameters in submicrometre-sized crystals from diffuse scattering in single-crystal electron diffraction data. The approach was demonstrated on Li1.2Ni0.13Mn0.54Co0.13O2, which is a state-of-the-art cathode material for lithium-ion batteries. The intensity distribution of the 1D diffuse scattering in the electron diffraction patterns of Li1.2Ni0.13Mn0.54Co0.13O2 depends on the number of stacking faults and twins in the crystal. A model of the disorder in Li1.2Ni0.13Mn0.54Co0.13O2 was developed and both the stacking fault probability and the percentage of the different twins in the crystal were refined using an evolutionary algorithm in DISCUS. The approach was applied on reciprocal space sections reconstructed from 3D electron diffraction data since they exhibit less dynamical effects compared with in-zone electron diffraction patterns. A good agreement was achieved between the calculated and the experimental intensity distribution of the diffuse scattering. The short-range order parameters in submicrometre-sized crystals can thus successfully be refined from the diffuse scattering in single-crystal electron diffraction data using an evolutionary algorithm in DISCUS.

BackgroundBefore starting surgery for the resection of an intracranial tumor, its outlines are typically marked on the skin of the patient. This allows for the planning of the optimal skin incision, craniotomy, and angle of approach. Conventionally, the surgeon determines tumor borders using neuronavigation with a tracked pointer. However, interpretation errors can lead to important deviations, especially for deep-seated tumors, potentially resulting in a suboptimal approach with incomplete exposure. Augmented reality (AR) allows displaying of the tumor and critical structures directly on the patient, which can simplify and improve surgical preparation.MethodsWe developed an AR-based workflow for intracranial tumor resection planning deployed on the Microsoft HoloLens II, which exploits the built-in infrared-camera for tracking the patient. We initially performed a phantom study to assess the accuracy of the registration and tracking. Following this, we evaluated the AR-based planning step in a prospective clinical study for patients undergoing resection of a brain tumor. This planning step was performed by 12 surgeons and trainees with varying degrees of experience. After patient registration, tumor outlines were marked on the patient's skin by different investigators, consecutively using a conventional neuronavigation system and an AR-based system. Their performance in both registration and delineation was measured in terms of accuracy and duration and compared.ResultsDuring phantom testing, registration errors remained below 2.0 mm and 2.0° for both AR-based navigation and conventional neuronavigation, with no significant difference between both systems. In the prospective clinical trial, 20 patients underwent tumor resection planning. Registration accuracy was independent of user experience for both AR-based navigation and the commercial neuronavigation system. AR-guided tumor delineation was deemed superior in 65% of cases, equally good in 30% of cases, and inferior in 5% of cases when compared to the conventional navigation system. The overall planning time (AR = 119 ± 44 s, conventional = 187 ± 56 s) was significantly reduced through the adoption of the AR workflow (p < 0.001), with an average time reduction of 39%.ConclusionBy providing a more intuitive visualization of relevant data to the surgeon, AR navigation provides an accurate method for tumor resection planning that is quicker and more intuitive than conventional neuronavigation. Further research should focus on intraoperative implementations.

If multiple-breath washout (MBW)-derived acinar ventilation heterogeneity (Sacin) really represents peripheral units, the N2 phase-III of the first MBW exhalation should be curvilinear. This is essentially due to the superposed effect of gas diffusion and convection resulting in an equilibration of N2 concentrations between neighboring lung units throughout exhalation. We investigated this in smokers with computed tomography (CT)-proven functional small airway disease. Instantaneous N2-slopes were computed over 40-ms intervals throughout phase-III and normalized by mean phase-III N2 concentration. N2 phase-III (concave) curvilinearity was quantified as the rate at which the instantaneous N2-slope decreases past the phase-II peak over a 1-s interval; for a linear N2 phase-III unaffected by diffusion, this rate would amount to 0 L−1/s. N2 phase-III curvilinearity was obtained on the experimental curves and on existing model simulations of N2 curves from a normal peripheral lung model and one with missing terminal bronchioles (either 50% or 30% TB left). In 46 smokers [66 (±8) yr; 49 (±26) pack·yr] with CT-based evidence of peripheral lung destruction, instantaneous N2-slope decrease was compared between those with (fSAD+fEmphys) > 20% [−0.26 ± 0.14 (SD) L−1/s; n = 24] and those with (fSAD+fEmphys) < 20% [−0.16 ± 0.12 (SD) L−1/s; n = 22] (P = 0.014). Experimental values fell in the range predicted by a realistic peripheral lung model with progressive reduction of terminal bronchioles: values of instantaneous N2-slope decrease obtained from model simulations were −0.09 L−1/s (normal lung; 100% TB left), −0.17 L−1/s (normal lung 50% TB left), and −0.29 L−1/s (30% TB left). In smokers with CT-based evidence of functional small airway alterations, it is possible to demonstrate that Sacin really does represent the most peripheral airspaces. NEW & NOTEWORTHY: In smokers with computed tomography-based evidence of functional small airway alterations by parametric response mapping, it is possible to demonstrate that the multiple-breath washout-derived Sacin, an index of acinar ventilation heterogeneity, actually does represent the most peripheral airspaces. This is done by verifying on experimental N2 washout curves of the first breath, N2 phase-III concavity predicted by the diffusion-convection interdependence model. [ABSTRACT FROM AUTHOR]

Background: Antibodies that inhibit the programmed cell death protein 1 (PD-1) receptor offer a significant survival benefit, potentially cure (i.e., durable disease-free survival following treatment discontinuation), a substantial proportion of patients with advanced melanoma. Most patients however fail to respond to such treatment or acquire resistance. Previously, we reported that baseline total metabolic tumour volume (TMTV) determined by whole-body [18F]FDG PET/CT was independently correlated with survival and able to predict the futility of treatment. Manual delineation of [18F]FDG-avid lesions is however labour intensive and not suitable for routine use. A predictive survival model is proposed based on automated analysis of baseline, whole-body [18F]FDG images. Methods: Lesions were segmented on [18F]FDG PET/CT using a deep-learning approach and derived features were investigated through Kaplan–Meier survival estimates with univariate logrank test and Cox regression analyses. Selected parameters were evaluated in multivariate Cox survival regressors. Results: In the development set of 69 patients, overall survival prediction based on TMTV, lactate dehydrogenase levels and presence of brain metastases achieved an area under the curve of 0.78 at one year, 0.70 at two years. No statistically significant difference was observed with respect to using manually segmented lesions. Internal validation on 31 patients yielded scores of 0.76 for one year and 0.74 for two years. Conclusions: Automatically extracted TMTV based on whole-body [18F]FDG PET/CT can aid in building predictive models that can support therapeutic decisions in patients treated with immune-checkpoint blockade.

Introduction: With increasing use of robotic surgical adjuncts, artificial intelligence and augmented reality in neurosurgery, the automated analysis of digital images and videos acquired over various procedures becomes a subject of increased interest. While several computer vision (CV) methods have been developed and implemented for analyzing surgical scenes, few studies have been dedicated to neurosurgery. Research question: In this work, we present a systematic literature review focusing on CV methodologies specifically applied to the analysis of neurosurgical procedures based on intra-operative images and videos. Additionally, we provide recommendations for the future developments of CV models in neurosurgery. Material and methods: We conducted a systematic literature search in multiple databases until January 17, 2023, including Web of Science, PubMed, IEEE Xplore, Embase, and SpringerLink. Results: We identified 17 studies employing CV algorithms on neurosurgical videos/images. The most common applications of CV were tool and neuroanatomical structure detection or characterization, and to a lesser extent, surgical workflow analysis. Convolutional neural networks (CNN) were the most frequently utilized architecture for CV models (65%), demonstrating superior performances in tool detection and segmentation. In particular, mask recurrent-CNN manifested most robust performance outcomes across different modalities. Discussion and conclusion: Our systematic review demonstrates that CV models have been reported that can effectively detect and differentiate tools, surgical phases, neuroanatomical structures, as well as critical events in complex neurosurgical scenes with accuracies above 95%. Automated tool recognition contributes to objective characterization and assessment of surgical performance, with potential applications in neurosurgical training and intra-operative safety management.

BackgroundPet robots are gaining momentum as a technology-based intervention to support the psychosocial wellbeing of people with dementia. Current research suggests that they can reduce agitation, improve mood and social engagement. The implementation of pet robots in care for persons with dementia raises several ethical debates. However, there is a paucity of empirical evidence to uncover care providers’ ethical intuitions, defined as individuals’ fundamental moral knowledge that are not underpinned by any specific propositions.ObjectivesExplore care professionals’ and organisational leaders’ ethical intuitions before and when implementing pet robots in nursing homes for routine dementia care.Materials and methodsWe undertook a secondary qualitative analysis of data generated from in-depth, semi-structured interviews with 22 care professionals and organisational leaders from eight nursing homes in Ireland. Data were analysed using reflexive thematic analysis. Ethical constructs derived from a comprehensive review of argument-based ethics literature were used to guide the deductive coding of concepts. An inductive approach was used to generate open codes not falling within the pre-existing concepts.FindingsEthical intuitions for implementing pet robots manifested at three levels: an (1) individual-relational, (2) organisational and (3) societal level. At the individual-relational level, ethical intuitions involved supporting the autonomy of residents and care providers, using the robots to alleviate residents’ social isolation, and the physical and psychosocial impacts associated with their use. Some care providers had differing sentiments about anthropomorphizing pet robots. At the organisational level, intuitions related to the use of pet robots to relieve care provision, changes to the organisational workflow, and varying extents of openness amongst care providers to use technological innovations. At the societal level, intuitions pertained conceptions of dementia care in nursing homes, and social justice relating to the affordability and availability of pet robots. Discrepancies between participants’ ethical intuitions and existing philosophical arguments were uncovered.ConclusionCare professionals and organisational leaders had different opinions on how pet robots are or should be implemented for residents with dementia. Future research should consider involving care practitioners, people with dementia, and their family members in the ethics dialogue to support the sustainable, ethical use of pet robots in practice.