Are artificial intelligence generated lower extremity radiographic measurements accurate in a cohort with implants?

Objective: Leg length discrepancy (LLD) and malalignment of the lower extremity can lead to pain and increased risk of osteoarthritis. Radiographic measurements on anteroposterior (AP) full-length radiographs can be used to assess LLD and lower extremity alignment. The primary aim of this study was to evaluate the accuracy of AI software in performing lower extremity radiographic measurements in patients with implants.

Breaking the silence: AI's contribution to detecting vertebral fractures in opportunistic CT scans in the elderly-a validation study.

Unlabelled: Vertebral fractures frequently go undetected in clinical practice. AI-assisted detection on CT scans demonstrates considerable promise, with a sensitivity of 86% and a specificity of 99%. The performance varied based on sex, and CT kernel, showing superior results in females and in scans using non-bone kernel protocols.

Reliability assessment of leg length and angular alignment on manual reads versus artificial intelligence-generated lower extremity radiographic measurements.

Purpose: Leg length discrepancy (LLD) and lower extremity malalignment can lead to pain and osteoarthritis. A variety of radiographic parameters are used to assess LLD and alignment. A 510(k) FDA approved artificial intelligence (AI) software locates landmarks on full leg standing radiographs and performs several measurements.

Retinotopic cortical mapping in objective functional monitoring of macular therapy.

Background/aims: To determine the suitability of functional MRI (fMRI) as an objective measure of macular function following therapeutic intervention; conventional psychophysical measures rely heavily on patient compliance.

Methods: Twenty patients with neovascular age-related macular degeneration (nAMD) were studied with high-resolution fMRI, visual acuity, reading accuracy and speed, contrast sensitivity (CS) and microperimetry (MP) before and after 3 monthly intravitreal injections of ranibizumab. Population-receptive field retinotopic maps calculated from fMRI data were compared with psychophysical measures and optical coherence tomography.

Artificial intelligence-based analyses of varus leg alignment and after high tibial osteotomy show high accuracy and reproducibility.

Purpose: The aim of this study was to investigate the performance of an artificial intelligence (AI)-based software for fully automated analysis of leg alignment pre- and postoperatively after high tibial osteotomy (HTO) on long-leg radiographs (LLRs).

Methods: Long-leg radiographs of 95 patients with varus malalignment that underwent medial open-wedge HTO were analyzed pre- and postoperatively. Three investigators and an AI software using deep learning algorithms (LAMA™, ImageBiopsy Lab, Vienna, Austria) evaluated the hip-knee-ankle angle (HKA), mechanical axis deviation (MAD), joint line convergence angle (JLCA), medial proximal tibial angle (MPTA), and mechanical lateral distal femoral angle (mLDFA).

Deep learning generated lower extremity radiographic measurements are adequate for quick assessment of knee angular alignment and leg length determination.

Purpose: Angular and longitudinal deformities of leg alignment create excessive stresses across joints, leading to pain and impaired function. Multiple measurements are used to assess these deformities on anteroposterior (AP) full-length radiographs. An artificial intelligence (AI) software automatically locates anatomical landmarks on AP full-length radiographs and performs 13 measurements to assess knee angular alignment and leg length.

Body height estimation from automated length measurements on standing long leg radiographs using artificial intelligence.

Artificial-intelligence (AI) allows large-scale analyses of long-leg-radiographs (LLRs). We used this technology to derive an update for the classical regression formulae by Trotter and Gleser, which are frequently used to infer stature based on long-bone measurements. We analyzed calibrated, standing LLRs from 4200 participants taken between 2015 and 2020.

Comparison of Stimulus Types for Retinotopic Cortical Mapping of Macular Disease.

Purpose: Retinotopic maps acquired using functional magnetic resonance imaging (fMRI) provide a valuable adjunct in the assessment of macular function at the level of the visual cortex. The present study quantitatively assessed the performance of different visual stimulation approaches for mapping visual field coverage.

Methods: Twelve patients with geographic atrophy (GA) secondary to age-related macular degeneration (AMD) were examined using high-resolution ultra-high field fMRI (Siemens Magnetom 7T) and microperimetry (MP; Nidek MP-3).

Deep Learning for Fully Automated Radiographic Measurements of the Pelvis and Hip.

The morphometry of the hip and pelvis can be evaluated in native radiographs. Artificial-intelligence-assisted analyses provide objective, accurate, and reproducible results. This study investigates the performance of an artificial intelligence (AI)-based software using deep learning algorithms to measure radiological parameters that identify femoroacetabular impingement and hip dysplasia.

Intrasession and Intersession Reproducibility of Artificial Scotoma pRF Mapping Results at Ultra-High Fields.

Functional magnetic resonance imaging (fMRI) combined with population receptive field (pRF) mapping allows for associating positions on the visual cortex to areas on the visual field. Apart from applications in healthy subjects, this method can also be used to examine dysfunctions in patients suffering from partial visual field losses. While such objective measurement of visual deficits (scotoma) is of great importance for, e.

Artificial intelligence-generated hip radiological measurements are fast and adequate for reliable assessment of hip dysplasia : an external validation study.

Aims: Hip dysplasia (HD) leads to premature osteoarthritis. Timely detection and correction of HD has been shown to improve pain, functional status, and hip longevity. Several time-consuming radiological measurements are currently used to confirm HD.

Fully automated deep learning for knee alignment assessment in lower extremity radiographs: a cross-sectional diagnostic study.

Objectives: Accurate assessment of knee alignment and leg length discrepancy is currently measured manually from standing long-leg radiographs (LLR), a process that is both time consuming and poorly reproducible. The aim was to assess the performance of a commercial available AI software by comparing its outputs with manually performed measurements.

Materials And Methods: The AI was trained on over 15,000 radiographs to measure various clinical angles and lengths from LLRs.

Combining stimulus types for improved coverage in population receptive field mapping.

Retinotopy experiments using population receptive field (pRF) mapping are ideal for assigning regions in the visual field to cortical brain areas. While various designs for visual stimulation were suggested in the literature, all have specific shortcomings regarding visual field coverage. Here we acquired high-resolution 7 Tesla fMRI data to compare pRF-based coverage maps obtained with the two most commonly used stimulus variants: moving bars; rotating wedges and expanding rings.

Technical Note: Human tissue-equivalent MRI phantom preparation for 3 and 7 Tesla.

Purpose: While test objects (phantoms) in magnetic resonance imaging (MRI) are crucial for sequence development, protocol validation, and quality control, studies on the preparation of phantoms have been scarce, particularly at fields exceeding 3 Tesla. Here, we present a framework for the preparation of phantoms with well-defined T and T times at 3 and 7 Tesla.

Methods: Phantoms with varying concentrations of agarose and Gd-DTPA were prepared and measured at 3 and 7 Tesla using T and T mapping techniques.

Beyond Sharing Unpleasant Affect-Evidence for Pain-Specific Opioidergic Modulation of Empathy for Pain.

It is not known how specific the neural mechanisms underpinning empathy for different domains are. In the present study, we set out to test whether shared neural representations between first-hand pain and empathy for pain are pain-specific or extend to empathy for unpleasant affective touch as well. Using functional magnetic resonance imaging and psychopharmacological experiments, we investigated if placebo analgesia reduces first-hand and empathic experiences of affective touch, and compared them with the effects on pain.

Placebo-induced pain reduction is associated with negative coupling between brain networks at rest.

Placebos can reduce pain by inducing beliefs in the effectiveness of an actually inert treatment. Such top-down effects on pain typically engage lateral and medial prefrontal regions, the insula, somatosensory cortex, as well as the thalamus and brainstem during pain anticipation or perception. Considering the level of large-scale brain networks, these regions spatially align with fronto-parietal/executive control, salience, and sensory-motor networks, but it is unclear if and how placebos alter interactions between them during rest.

Association between dynamic resting-state functional connectivity and ketamine plasma levels in visual processing networks.

Numerous studies demonstrate ketamine's influence on resting-state functional connectivity (rsFC). Seed-based and static rsFC estimation methods may oversimplify FC. These limitations can be addressed with whole-brain, dynamic rsFC estimation methods.

Correspondence between retinotopic cortical mapping and conventional functional and morphological assessment of retinal disease.

Purpose: The present study describes retinotopic mapping of the primary visual cortex using functional MRI (fMRI) in patients with retinal disease. It addresses the relationship between fMRI data and data obtained by conventional assessment including microperimetry (MP) and structural imaging.

Methods: Initial testing involved eight patients with central retinal disease (Stargardt disease, STGD) and eight with peripheral retinal disease (retinitis pigmentosa, RP), who were examined using fMRI and MP (Nidek MP-1).

Towards understanding rTMS mechanism of action: Stimulation of the DLPFC causes network-specific increase in functional connectivity.

Transcranial magnetic stimulation (TMS) is a powerful non-invasive technique for the modulation of brain activity. While the precise mechanism of action is still unknown, TMS is applied in cognitive neuroscience to establish causal relationships between stimulation and subsequent changes in cerebral function and behavioral outcome. In addition, TMS is an FDA-approved therapeutic agent in psychiatric disorders, especially major depression.

Effects of testosterone treatment on hypothalamic neuroplasticity in female-to-male transgender individuals.

Diffusion-weighted imaging (DWI) is used to measure gray matter tissue density and white matter fiber organization/directionality. Recent studies show that DWI also allows for assessing neuroplastic adaptations in the human hypothalamus. To this end, we investigated a potential influence of testosterone replacement therapy on hypothalamic microstructure in female-to-male (FtM) transgender individuals.

Effects of sex hormone treatment on white matter microstructure in individuals with gender dysphoria.

Sex steroid hormones such as estradiol and testosterone are known to have organizing, as well as activating effects on neural tissue in animals and humans. This study investigated the effects of transgender hormone replacement therapy on white matter microstructure using diffusion tensor imaging. Female-to-male and male-to-female transgender participants were measured at baseline, four weeks and four months past treatment start and compared to female and male controls.

The selfless mind: How prefrontal involvement in mentalizing with similar and dissimilar others shapes empathy and prosocial behavior.

Engaging in mentalizing, i.e., reflecting on others' thoughts, beliefs and feelings, is known to facilitate later empathy and prosocial behavior.

Subcortical gray matter changes in transgender subjects after long-term cross-sex hormone administration.

Sex-steroid hormones are primarily involved in sexual differentiation and development and are thought to underlie processes related to cognition and emotion. However, divergent results have been reported concerning the effects of hormone administration on brain structure including side effects like brain atrophy and dementia. Cross-sex hormone therapy in transgender subjects offers a unique model for studying the effects of sex hormones on the living human brain.

Testosterone affects language areas of the adult human brain.

Although the sex steroid hormone testosterone is integrally involved in the development of language processing, ethical considerations mostly limit investigations to single hormone administrations. To circumvent this issue we assessed the influence of continuous high-dose hormone application in adult female-to-male transsexuals. Subjects underwent magnetic resonance imaging before and after 4 weeks of testosterone treatment, with each scan including structural, diffusion weighted and functional imaging.

Placebo analgesia and its opioidergic regulation suggest that empathy for pain is grounded in self pain.

Empathy for pain activates brain areas partially overlapping with those underpinning the first-hand experience of pain. It remains unclear, however, whether such shared activations imply that pain empathy engages similar neural functions as first-hand pain experiences. To overcome the limitations of previous neuroimaging research, we pursued a conceptually novel approach: we used the phenomenon of placebo analgesia to experimentally reduce the first-hand experience of pain, and assessed whether this results in a concomitant reduction of empathy for pain.