Preliminary Analysis of Functional Variability in the Mousterian of Levallois Facies: A Reexamination

Author Institution: Department of Antrhopology, Case Western Reserve UniversityAn integral part of the New Archeology is a method of dealing with lithic variabilities based on a behavioral model and the use of mathematical techniques for the analysis of variance. To test some of the underlying assumptions of this paradigm a factor analysis was performed on published data for several Russian Mousterian sites. Seven factors were produced, and their content was interpreted as indicating two different types of activity: base camp killing and butchering and work camp transient food preparation

Reliability of clinically relevant 3D foot bone angles from quantitative computed tomography

BACKGROUND: Surgical treatment and clinical management of foot pathology requires accurate, reliable assessment of foot deformities. Foot and ankle deformities are multi-planar and therefore difficult to quantify by standard radiographs. Three-dimensional (3D) imaging modalities have been used to define bone orientations using inertial axes based on bone shape, but these inertial axes can fail to mimic established bone angles used in orthopaedics and clinical biomechanics. To provide improved clinical relevance of 3D bone angles, we developed techniques to define bone axes using landmarks on quantitative computed tomography (QCT) bone surface meshes. We aimed to assess measurement precision of landmark-based, 3D bone-to-bone orientations of hind foot and lesser tarsal bones for expert raters and a template-based automated method. METHODS: Two raters completed two repetitions each for twenty feet (10 right, 10 left), placing anatomic landmarks on the surfaces of calcaneus, talus, cuboid, and navicular. Landmarks were also recorded using the automated, template-based method. For each method, 3D bone axes were computed from landmark positions, and Cardan sequences produced sagittal, frontal, and transverse plane angles of bone-to-bone orientations. Angular reliability was assessed using intraclass correlation coefficients (ICCs) and the root mean square standard deviation (RMS-SD) for intra-rater and inter-rater precision, and rater versus automated agreement. RESULTS: Intra- and inter-rater ICCs were generally high (≥ 0.80), and the ICCs for each rater compared to the automated method were similarly high. RMS-SD intra-rater precision ranged from 1.4 to 3.6° and 2.4 to 6.1°, respectively, for the two raters, which compares favorably to uni-planar radiographic precision. Greatest variability was in Navicular: Talus sagittal plane angle and Cuboid: Calcaneus frontal plane angle. Precision of the automated, atlas-based template method versus the raters was comparable to each rater’s internal precision. CONCLUSIONS: Intra- and inter-rater precision suggest that the landmark-based methods have adequate test-retest reliability for 3D assessment of foot deformities. Agreement of the automated, atlas-based method with the expert raters suggests that the automated method is a valid, time-saving technique for foot deformity assessment. These methods have the potential to improve diagnosis of foot and ankle pathologies by allowing multi-planar quantification of deformities

Progression of foot deformity in charcot neuropathic osteoarthropathy

BACKGROUND: Charcot neuropathic osteoarthropathy associated foot deformity can result in joint instability, ulceration, and even amputation. The purpose of the present study was to follow patients with and without active Charcot osteoarthropathy for as long as two years to examine the magnitude and timing of foot alignment changes. METHODS: We studied fifteen subjects with Charcot osteoarthropathy and nineteen subjects with diabetes mellitus and peripheral neuropathy without Charcot osteoarthropathy for one year; eight of the subjects with osteoarthropathy and five of the subjects with diabetes and peripheral neuropathy were followed for two years. Bilateral weight-bearing radiographs of the foot were made at baseline for all subjects, with repeat radiographs being made at six months for the osteoarthropathy group and at one and two years for both groups. Radiographic measurements included the Meary angle, cuboid height, calcaneal pitch, and hindfoot-forefoot angle. RESULTS: The Meary angle, cuboid height, and calcaneal pitch worsened in feet with Charcot osteoarthropathy over one year as compared with the contralateral, uninvolved feet and feet in patients with diabetes and peripheral neuropathy. Cuboid height continued to worsen over the two-year follow-up in the feet with Charcot osteoarthropathy. These feet also had a greater change in the hindfoot-forefoot angle at one year as compared with the feet in patients with diabetes and peripheral neuropathy and at two years as compared with the contralateral, uninvolved feet. CONCLUSIONS: In patients with Charcot neuropathic osteoarthropathy, radiographic alignment measurements demonstrate the presence of foot deformity at the time of the initial clinical presentation and evidence of progressive changes over the first and second years. The six-month data suggest worsening of medial column alignment prior to lateral column worsening. This radiographic evidence of worsening foot alignment over time supports the need for aggressive intervention (conservative bracing or surgical fixation) to attempt to prevent limb-threatening complications. LEVEL OF EVIDENCE: Prognostic Level II. See Instructions for Authors for a complete description of levels of evidence

Neuropathic midfoot deformity: Associations with ankle and subtalar joint motion

BACKGROUND: Neuropathic deformities impair foot and ankle joint mobility, often leading to abnormal stresses and impact forces. The purpose of our study was to determine differences in radiographic measures of hind foot alignment and ankle joint and subtalar joint motion in participants with and without neuropathic midfoot deformities and to determine the relationships between radiographic measures of hind foot alignment to ankle and subtalar joint motion in participants with and without neuropathic midfoot deformities. METHODS: Sixty participants were studied in three groups. Forty participants had diabetes mellitus (DM) and peripheral neuropathy (PN) with 20 participants having neuropathic midfoot deformity due to Charcot neuroarthropathy (CN), while 20 participants did not have deformity. Participants with diabetes and neuropathy with and without deformity were compared to 20 young control participants without DM, PN or deformity. Talar declination and calcaneal inclination angles were assessed on lateral view weight bearing radiograph. Ankle dorsiflexion, plantar flexion and subtalar inversion and eversion were assessed by goniometry. RESULTS: Talar declination angle averaged 34±9, 26±4 and 23±3 degrees in participants with deformity, without deformity and young control participants, respectively (p< 0.010). Calcaneal inclination angle averaged 11±10, 18±9 and 21±4 degrees, respectively (p< 0.010). Ankle plantar flexion motion averaged 23±11, 38±10 and 47±7 degrees (p<0.010). The association between talar declination and calcaneal inclination angles with ankle plantar flexion range of motion is strongest in participants with neuropathic midfoot deformity. Participants with talonavicular and calcaneocuboid dislocations result in the most severe restrictions in ankle joint plantar flexion and subtalar joint inversion motions. CONCLUSIONS: An increasing talar declination angle and decreasing calcaneal inclination angle is associated with decreases in ankle joint plantar flexion motion in individuals with neuropathic midfoot deformity due to CN that may contribute to excessive stresses and ultimately plantar ulceration of the midfoot

Informatics and data mining tools and strategies for the Human Connectome Project

The Human Connectome Project (HCP) is a major endeavor that will acquire and analyze connectivity data plus other neuroimaging, behavioral, and genetic data from 1,200 healthy adults. It will serve as a key resource for the neuroscience research community, enabling discoveries of how the brain is wired and how it functions in different individuals. To fulfill its potential, the HCP consortium is developing an informatics platform that will handle: 1) storage of primary and processed data, 2) systematic processing and analysis of the data, 3) open access data sharing, and 4) mining and exploration of the data. This informatics platform will include two primary components. ConnectomeDB will provide database services for storing and distributing the data, as well as data analysis pipelines. Connectome Workbench will provide visualization and exploration capabilities. The platform will be based on standard data formats and provide an open set of application programming interfaces (APIs) that will facilitate broad utilization of the data and integration of HCP services into a variety of external applications. Primary and processed data generated by the HCP will be openly shared with the scientific community, and the informatics platform will be available under an open source license. This paper describes the HCP informatics platform as currently envisioned and places it into the context of the overall HCP vision and agenda

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

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