A theoretical and numerical study of a phase field higher-order active contour model of directed networks.

We address the problem of quasi-automatic extraction of directed networks, which have characteristic geometric features, from images. To include the necessary prior knowledge about these geometric features, we use a phase field higher-order active contour model of directed networks. The model has a large number of unphysical parameters (weights of energy terms), and can favour different geometric structures for different parameter values. To overcome this problem, we perform a stability analysis of a long, straight bar in order to find parameter ranges that favour networks. The resulting constraints necessary to produce stable networks eliminate some parameters, replace others by physical parameters such as network branch width, and place lower and upper bounds on the values of the rest. We validate the theoretical analysis via numerical experiments, and then apply the model to the problem of hydrographic network extraction from multi-spectral VHR satellite images

D-lactate is a promising biomarker for the diagnosis of periprosthetic joint infection

IntroductionReliable biomarkers for the diagnosis of periprosthetic joint infection (PJI) are of paramount clinical value. To date, synovial fluid leukocyte count is the standard surrogate parameter indicating PJI. As D-lactate is almost solely produced by bacteria, it represents a promising molecule in the diagnostic workflow of PJI evaluation. Therefore, the purpose of this study was to assess the performance of synovial fluid D-lactate for diagnosing PJI of the hip and knee.Materials and MethodsThese are preliminary results of a prospective multicenter study from one academic center. Seventy-two consecutive patients after total hip arthroplasty (THA) or total knee arthroplasty (TKA) were prospectively included. All patients received a joint aspiration in order to rule out or confirm PJI, which was diagnosed according to previously published institutional criteria. Synovial fluid D-lactate was determined spectrophotometrically at 450 nm. Receiver operating characteristic (ROC) analysis was performed to assess the diagnostic performance.ResultsEighteen patients (25%) were diagnosed with PJI and 54 patients (75%) were classified as aseptic. Synovial fluid D-lactate showed a sensitivity of 90.7% (95% CI: 79.7%–96.9%) and specificity of 83.3% (95% CI: 58.6%–96.4%) at a cut-off of 0.04 mmol/L. The median concentration of D-lactate was significantly higher in patients with PJI than in those with aseptic conditions (0.048 mmol/L, range, 0.026–0.076 mmol/L vs. 0.024 mmol/L, range, 0.003–0.058 mmol/L, p < 0.0001). The predominat microogranisms were staphylococci, followed by streptococci and gram-negative bacteria.ConclusionD-lactate bears a strong potential to act as a valuable biomarker for diagnosing PJI of the hip and knee. In our study, a cutoff of 0.04 mmol/L showed a comparable sensitivity to synovial fluid leukocyte count. However, its specificity was higher compared to conventional diagnostic tools. The additional advantages of D-lactate testing are requirement of low synovial fluid volume, short turnaround time and low cost

An image analysis toolbox for high-throughput C. elegans assays

We present a toolbox for high-throughput screening of image-based Caenorhabditis elegans phenotypes. The image analysis algorithms measure morphological phenotypes in individual worms and are effective for a variety of assays and imaging systems. This WormToolbox is available through the open-source CellProfiler project and enables objective scoring of whole-worm high-throughput image-based assays of C. elegans for the study of diverse biological pathways that are relevant to human disease.National Institutes of Health (U.S.) (U54 EB005149

Troublesome Heterotopic Ossification after Central Nervous System Damage: A Survey of 570 Surgeries

BACKGROUND: Heterotopic ossification (HO) is a frequent complication after central nervous system (CNS) damage but has seldom been studied. We aimed to investigate features of HO for the first time in a large sample and the rate of early recurrence of HO in terms of the time of surgery. METHODOLOGY/PRINCIPAL FINDINGS: We retrospectively analyzed data from an anonymous prospective survey of patients undergoing surgery between May 1993 and November 2009 in our institution for troublesome HO related to acquired neurological disease. Demographic and HO characteristics and neurological etiologies were recorded. For 357 consecutive patients, we collected data on 539 first surgeries for HO (129 surgeries for multiple sites). During the follow-up, recurrences requiring another surgery appeared in 31 cases (5.8% [31/539]; 95% confidence interval [CI]: 3.8%-7.8%; 27 patients). Most HO requiring surgery occurred after traumatic brain injury (199 patients [55.7%]), then spinal cord injury (86 [24.0%]), stroke (42 [11.8%]) and cerebral anoxia (30 [8.6%]). The hip was the primary site of HO (328 [60.9%]), then the elbow (115 [21.3%]), knee (77 [14.3%]) and shoulder (19 [3.5%]). For all patients, 181 of the surgeries were performed within the first year after the CNS damage, without recurrence of HO. Recurrence was not associated with etiology (p = 0.46), sex (p = 1.00), age at CNS damage (p = 0.2), multisite localization (p = 0.34), or delay to surgery (p = 0.7). CONCLUSIONS/SIGNIFICANCE: In patients with CNS damage, troublesome HO and recurrence occurs most frequently after traumatic brain injury and appears frequently in the hip and elbow. Early surgery for HO is not a factor of recurrence

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

Future-ai:International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

Joint Segmentation of Image Ensembles via Latent Atlases

Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. However, the availability of comprehensive, reliable and suitable manual segmentations for atlas construction is limited. We therefore propose a joint segmentation of corresponding, aligned structures in the entire population that does not require a probability atlas. Instead, a latent atlas, initialized by a single manual segmentation, is inferred from the evolving segmentations of the ensemble. The proposed method is based on probabilistic principles but is solved using partial differential equations (PDEs) and energy minimization criteria. We evaluate the method by segmenting 50 brain MR volumes. Segmentation accuracy for cortical and subcortical structures approaches the quality of state-of-the-art atlas-based segmentation results, suggesting that the latent atlas method is a reasonable alternative when existing atlases are not compatible with the data to be processed

Swiss pig farmer's perception and usage of antibiotics during fattering period

The increasing problem of antibiotic resistance has been related, among other factors, to the extensive use of antibiotics in pig farming. To be able to reduce antibiotic usage in this type of livestock breeding, it may be important to know which internal characteristics of farmers are related to their antibiotic treatment practices, next to the external characteristics, such as husbandry conditions. We conducted a survey among Swiss farmers of fattening pigs to investigate their attitudes and habits related to antibiotic usage, as well as their perception of antibiotic resistance and of policy measures intended to reduce antibiotic usage in pig farming. In addition, we related farmers׳ personal variables, attitudes and habits, and their farm characteristics to their actual antibiotic usage at three time points during the fattening period. The farmers in our study appeared to be little aware of the risks of antibiotic usage in pig husbandry. Results seemed to imply that different attitudes and habits were related to antibiotic usage at the three time points during fattening. At the beginning of the fattening period, the farmers׳ habits were mainly related to their antibiotic usage. Perceived risks and cautious behaviour regarding antibiotics were important predictors of farmers׳ perceived impact of policy measures to reduce antibiotic usage. We suggest that promoting habits that reduce antibiotic use among farmers and increasing their risk awareness related to antibiotics in pig husbandry will most likely reduce the use of these substances in pig farming