Influence of deep learning image reconstruction algorithm for reducing radiation dose and image noise compared to iterative reconstruction and filtered back projection for head and chest computed tomography examinations: a systematic review.

Background: The most recent advances in Computed Tomography (CT) image reconstruction technology are Deep learning image reconstruction (DLIR) algorithms. Due to drawbacks in Iterative reconstruction (IR) techniques such as negative image texture and nonlinear spatial resolutions, DLIRs are gradually replacing them. However, the potential use of DLIR in Head and Chest CT has to be examined further. Hence, the purpose of the study is to review the influence of DLIR on Radiation dose (RD), Image noise (IN), and outcomes of the studies compared with IR and FBP in Head and Chest CT examinations. Methods: We performed a detailed search in PubMed, Scopus, Web of Science, Cochrane Library, and Embase to find the articles reported using DLIR for Head and Chest CT examinations between 2017 to 2023. Data were retrieved from the short-listed studies using Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. Results: Out of 196 articles searched, 15 articles were included. A total of 1292 sample size was included. 14 articles were rated as high and 1 article as moderate quality. All studies compared DLIR to IR techniques. 5 studies compared DLIR with IR and FBP. The review showed that DLIR improved IQ, and reduced RD and IN for CT Head and Chest examinations. Conclusions: DLIR algorithm have demonstrated a noted enhancement in IQ with reduced IN for CT Head and Chest examinations at lower dose compared with IR and FBP. DLIR showed potential for enhancing patient care by reducing radiation risks and increasing diagnostic accuracy.

Neuromesodermal specification during head-to-tail body axis formation.

The anterior-to-posterior (head-to-tail) body axis is extraordinarily diverse among vertebrates but conserved within species. Body axis development requires a population of axial progenitors that resides at the posterior of the embryo to sustain elongation and is then eliminated once axis extension is complete. These progenitors occupy distinct domains in the posterior (tail-end) of the embryo and contribute to various lineages along the body axis. The subset of axial progenitors with neuromesodermal competency will generate both the neural tube (the precursor of the spinal cord), and the trunk and tail somites (producing the musculoskeleton) during embryo development. These axial progenitors are called Neuromesodermal Competent cells (NMCs) and Neuromesodermal Progenitors (NMPs). NMCs/NMPs have recently attracted interest beyond the field of developmental biology due to their clinical potential. In the mouse, the maintenance of neuromesodermal competency relies on a fine balance between a trio of known signals: Wnt/ß-catenin, FGF signalling activity and suppression of retinoic acid signalling. These signals regulate the relative expression levels of the mesodermal transcription factor Brachyury and the neural transcription factor Sox2, permitting the maintenance of progenitor identity when co-expressed, and either mesoderm or neural lineage commitment when the balance is tilted towards either Brachyury or Sox2, respectively. Despite important advances in understanding key genes and cellular behaviours involved in these fate decisions, how the balance between mesodermal and neural fates is achieved remains largely unknown. In this chapter, we provide an overview of signalling and gene regulatory networks in NMCs/NMPs. We discuss mutant phenotypes associated with axial defects, hinting at the potential significant role of lesser studied proteins in the maintenance and differentiation of the progenitors that fuel axial elongation.

Comparative analysis of GoPro and digital cameras in head and neck flap harvesting surgery video documentation: an innovative and efficient method for surgical education.

BACKGROUND: An urgent need exists for innovative surgical video recording techniques in head and neck reconstructive surgeries, particularly in low- and middle-income countries where a surge in surgical procedures necessitates more skilled surgeons. This demand, significantly intensified by the COVID-19 pandemic, highlights the critical role of surgical videos in medical education. We aimed to identify a straightforward, high-quality approach to recording surgical videos at a low economic cost in the operating room, thereby contributing to enhanced patient care. METHODS: The recording was comprised of six head and neck flap harvesting surgeries using GoPro or two types of digital cameras. Data were extracted from the recorded videos and their subsequent editing process. Some of the participants were subsequently interviewed. RESULTS: Both cameras, set at 4 K resolution and 30 frames per second (fps), produced satisfactory results. The GoPro, worn on the surgeon's head, moves in sync with the surgeon, offering a unique first-person perspective of the operation without needing an additional assistant. Though cost-effective and efficient, it lacks a zoom feature essential for close-up views. In contrast, while requiring occasional repositioning, the digital camera captures finer anatomical details due to its superior image quality and zoom capabilities. CONCLUSION: Merging these two systems could significantly advance the field of surgical video recording. This innovation holds promise for enhancing technical communication and bolstering video-based medical education, potentially addressing the global shortage of specialized surgeons.

Using your head - cranial steering in pterosaurs.

The vast majority of pterosaurs are characterized by relatively large, elongate heads that are often adorned with large, elaborate crests. Projecting out in front of the body, these large heads and any crests must have had an aerodynamic effect. The working hypothesis of the present study is that these oversized heads were used to control the left-right motions of the body during flight. Using digital models of eight non-pterodactyloids ("rhamphorhyncoids") and ten pterodactyloids, the turning moments associated with the head + neck show a close and consistent correspondence with the rotational inertia of the whole body about a vertical axis in both groups, supporting the idea of a functional relationship. Turning moments come from calculating the lateral area of the head (plus any crests) and determining the associated lift (aerodynamic force) as a function of flight speed, with flight speeds being based on body mass. Rotational inertias were calculated from the three-dimensional mass distribution of the axial body, the limbs, and the flight membranes. The close correlation between turning moment and rotational inertia was used to revise the life restorations of two pterosaurs and to infer relatively lower flight speeds in another two.

PSFHS: Intrapartum ultrasound image dataset for AI-based segmentation of pubic symphysis and fetal head.

During the process of labor, the intrapartum transperineal ultrasound examination serves as a valuable tool, allowing direct observation of the relative positional relationship between the pubic symphysis and fetal head (PSFH). Accurate assessment of fetal head descent and the prediction of the most suitable mode of delivery heavily rely on this relationship. However, achieving an objective and quantitative interpretation of the ultrasound images necessitates precise PSFH segmentation (PSFHS), a task that is both time-consuming and demanding. Integrating the potential of artificial intelligence (AI) in the field of medical ultrasound image segmentation, the development and evaluation of AI-based models rely significantly on access to comprehensive and meticulously annotated datasets. Unfortunately, publicly accessible datasets tailored for PSFHS are notably scarce. Bridging this critical gap, we introduce a PSFHS dataset comprising 1358 images, meticulously annotated at the pixel level. The annotation process adhered to standardized protocols and involved collaboration among medical experts. Remarkably, this dataset stands as the most expansive and comprehensive resource for PSFHS to date.

Interdisciplinary Management of Vascular Anomalies in the Head and Neck. / Interdisziplinäres Management vaskulärer Anomalien im Kopf-Hals-Bereich.

Vascular anomalies in the head and neck area are usually rare diseases and pose a particular diagnostic and therapeutic challenge. They are divided into vascular tumours and vascular malformations. A distinction is made between benign tumours, such as infantile haemangioma, and rare malignant tumours, such as angiosarcoma. Vascular malformations are categorised as simple malformations, mixed malformations, large vessel anomalies and those associated with other anomalies. Treatment is interdisciplinary and various modalities are available. These include clinical observation, sclerotherapy, embolisation, ablative and coagulating procedures, surgical resection and systemic drug therapy. Treatment is challenging, as vascular anomalies in the head and neck region practically always affect function and aesthetics. A better understanding of the genetic and molecular biological basis of vascular anomalies has recently led to clinical research into targeted drug therapies. This article provides an up-to-date overview of the diagnosis, clinic and treatment of vascular anomalies in the head and neck region.

Unsupervised machine learning for clustering forward head posture, protraction and retraction movement patterns based on craniocervical angle data in individuals with nonspecific neck pain.

OBJECTIVES: The traditional understanding of craniocervical alignment emphasizes specific anatomical landmarks. However, recent research has challenged the reliance on forward head posture as the primary diagnostic criterion for neck pain. An advanced relationship exists between neck pain and craniocervical alignment, which requires a deeper exploration of diverse postures and movement patterns using advanced techniques, such as clustering analysis. We aimed to explore the complex relationship between craniocervical alignment, and neck pain and to categorize alignment patterns in individuals with nonspecific neck pain using the K-means algorithm. METHODS: This study included 229 office workers with nonspecific neck pain who applied unsupervised machine learning techniques. The craniocervical angles (CCA) during rest, protraction, and retraction were measured using two-dimensional video analysis, and neck pain severity was assessed using the Northwick Park Neck Pain Questionnaire (NPQ). CCA during sitting upright in a comfortable position was assessed to evaluate the resting CCA. The average of midpoints between repeated protraction and retraction measures was considered as the midpoint CCA. The K-means algorithm helped categorize participants into alignment clusters based on age, sex and CCA data. RESULTS: We found no significant correlation between NPQ scores and CCA data, challenging the traditional understanding of neck pain and alignment. We observed a significant difference in age (F = 140.14, p < 0.001), NPQ total score (F = 115.83, p < 0.001), resting CCA (F = 79.22, p < 0.001), CCA during protraction (F = 33.98, p < 0.001), CCA during retraction (F = 40.40, p < 0.001), and midpoint CCA (F = 66.92, p < 0.001) among the three clusters and healthy controls. Cluster 1 was characterized by the lowest resting and midpoint CCA, and CCA during pro- and -retraction, indicating a significant forward head posture and a pattern of retraction restriction. Cluster 2, the oldest group, showed CCA measurements similar to healthy controls, yet reported the highest NPQ scores. Cluster 3 exhibited the highest CCA during protraction and retraction, suggesting a limitation in protraction movement. DISCUSSION: Analyzing 229 office workers, three distinct alignment patterns were identified, each with unique postural characteristics; therefore, treatments addressing posture should be individualized and not generalized across the population.

RTSeg-net: A lightweight network for real-time segmentation of fetal head and pubic symphysis from intrapartum ultrasound images.

The segmentation of the fetal head (FH) and pubic symphysis (PS) from intrapartum ultrasound images plays a pivotal role in monitoring labor progression and informing crucial clinical decisions. Achieving real-time segmentation with high accuracy on systems with limited hardware capabilities presents significant challenges. To address these challenges, we propose the real-time segmentation network (RTSeg-Net), a groundbreaking lightweight deep learning model that incorporates innovative distribution shifting convolutional blocks, tokenized multilayer perceptron blocks, and efficient feature fusion blocks. Designed for optimal computational efficiency, RTSeg-Net minimizes resource demand while significantly enhancing segmentation performance. Our comprehensive evaluation on two distinct intrapartum ultrasound image datasets reveals that RTSeg-Net achieves segmentation accuracy on par with more complex state-of-the-art networks, utilizing merely 1.86 M parameters-just 6 % of their hyperparameters-and operating seven times faster, achieving a remarkable rate of 31.13 frames per second on a Jetson Nano, a device known for its limited computing capacity. These achievements underscore RTSeg-Net's potential to provide accurate, real-time segmentation on low-power devices, broadening the scope for its application across various stages of labor. By facilitating real-time, accurate ultrasound image analysis on portable, low-cost devices, RTSeg-Net promises to revolutionize intrapartum monitoring, making sophisticated diagnostic tools accessible to a wider range of healthcare settings.

Improved hypertensive stroke classification based on multi-scale feature fusion of head axial CT angiogram and multimodal learning.

PURPOSE: Strokes are severe cardiovascular and circulatory diseases with two main types: ischemic and hemorrhagic. Clinically, brain images such as computed tomography (CT) and computed tomography angiography (CTA) are widely used to recognize stroke types. However, few studies have combined imaging and clinical data to classify stroke or consider a factor as an Independent etiology. METHODS: In this work, we propose a classification model that automatically distinguishes stroke types with hypertension as an independent etiology based on brain imaging and clinical data. We first present a preprocessing workflow for head axial CT angiograms, including noise reduction and feature enhancement of the images, followed by an extraction of regions of interest. Next, we develop a multi-scale feature fusion model that combines the location information of position features and the semantic information of deep features. Furthermore, we integrate brain imaging with clinical information through a multimodal learning model to achieve more reliable results. RESULTS: Experimental results show our proposed models outperform state-of-the-art models on real imaging and clinical data, which reveals the potential of multimodal learning in brain disease diagnosis. CONCLUSION: The proposed methodologies can be extended to create AI-driven diagnostic assistance technology for categorizing strokes.

Measurement proprieties of the CROM instrument for assessing head posture, neck retraction and protraction.

BACKGROUND: The CROM instrument is widely used clinically and in research to measure neck range of motion. However, its measurement proprieties during the assessment of protraction and retraction movements were not examined so far. OBJECTIVE: To analyse the intra- and inter-rater reliability, the concurrent validity of the CROM for measuring head posture, retraction and protraction in healthy subjects. METHODS: Thirty-three asymptomatic subjects were recruited and assigned in a random order to one of two raters. After a 10-min break, they were examined by a second rater (Assessment 1). After a 30-min break, both raters repeated the examination (Assessment 2). The examination consisted of measuring the head posture, maximum head protraction and maximum retraction. Each movement was repeated 3 times and measured simultaneously with the CROM and with a 3D capture system laboratory. RESULTS: The intra-rater reliability of the CROM was excellent for both raters for head posture and all head movements (ICC>0.9, 95% CI: 0.82-0.99, p < 0.01). The inter-rater reliability was excellent for head posture (ICC>0.95, 95% CI: 0.92-0.98, p < 0.01) and good-to-excellent for all movements at both time-points (ICC = 0.73-0.98, 95%CI: 0.45-0.99, p < 0.01). The validity analysis showed moderate-to-strong correlation between instruments for the head posture and head movements [(r) = -0.47 to -0.78), 95% CI: 0.99 to -0.24, p < 0.01]. CONCLUSION: The CROM instrument has good-to-excellent reliability and adequate validity for measuring cervical position and displacement in the sagittal plane.

Flat Head Syndrome in Infants.

This JAMA Patient Page describes the types of flat head syndrome and its prevention and treatment.

Understanding the effects of stress on the P300 response during naturalistic simulation of heights exposure.

Stress is a prevalent bodily response universally experienced and significantly affects a person's mental and cognitive state. The P300 response is a commonly observed brain behaviour that provides insight into a person's cognitive state. Previous works have documented the effects of stress on the P300 behaviour; however, only a few have explored the performance in a mobile and naturalistic experimental setup. Our study examined the effects of stress on the human brain's P300 behaviour through a height exposure experiment that incorporates complex visual, vestibular, and proprioceptive stimuli. A more complex sensory environment could produce translatable findings toward real-world behaviour and benefit emerging technologies such as brain-computer interfaces. Seventeen participants experienced our experiment that elicited the stress response through physical and virtual height exposure. We found two unique groups within our participants that exhibited contrasting behavioural performance and P300 target reaction response when exposed to stressors (from walking at heights). One group performed worse when exposed to heights and exhibited a significant decrease in parietal P300 peak amplitude and increased beta and gamma power. On the other hand, the group less affected by stress exhibited a change in their N170 peak amplitude and alpha/mu rhythm desynchronisation. The findings of our study suggest that a more individualised approach to assessing a person's behaviour performance under stress can aid in understanding P300 performance when experiencing stress.

Estimate and compensate head motion in non-contrast head CT scans using partial angle reconstruction and deep learning.

BACKGROUND: Patient head motion is a common source of image artifacts in computed tomography (CT) of the head, leading to degraded image quality and potentially incorrect diagnoses. The partial angle reconstruction (PAR) means dividing the CT projection into several consecutive angular segments and reconstructing each segment individually. Although motion estimation and compensation using PAR has been developed and investigated in cardiac CT scans, its potential for reducing motion artifacts in head CT scans remains unexplored. PURPOSE: To develop a deep learning (DL) model capable of directly estimating head motion from PAR images of head CT scans and to integrate the estimated motion into an iterative reconstruction process to compensate for the motion. METHODS: Head motion is considered as a rigid transformation described by six time-variant variables, including the three variables for translation and three variables for rotation. Each motion variable is modeled using a B-spline defined by five control points (CP) along time. We split the full projections from 360° into 25 consecutive PARs and subsequently input them into a convolutional neural network (CNN) that outputs the estimated CPs for each motion variable. The estimated CPs are used to calculate the object motion in each projection, which are incorporated into the forward and backprojection of an iterative reconstruction algorithm to reconstruct the motion-compensated image. The performance of our DL model is evaluated through both simulation and phantom studies. RESULTS: The DL model achieved high accuracy in estimating head motion, as demonstrated in both the simulation study (mean absolute error (MAE) ranging from 0.28 to 0.45 mm or degree across different motion variables) and the phantom study (MAE ranging from 0.40 to 0.48 mm or degree). The resulting motion-corrected image, I D L , P A R ${I}_{DL,\ PAR}$ , exhibited a significant reduction in motion artifacts when compared to the traditional filtered back-projection reconstructions, which is evidenced both in the simulation study (image MAE drops from 178 ± $ \pm $ 33HU to 37 ± $ \pm $ 9HU, structural similarity index (SSIM) increases from 0.60 ± $ \pm $ 0.06 to 0.98 ± $ \pm $ 0.01) and the phantom study (image MAE drops from 117 ± $ \pm $ 17HU to 42 ± $ \pm $ 19HU, SSIM increases from 0.83 ± $ \pm $ 0.04 to 0.98 ± $ \pm $ 0.02). CONCLUSIONS: We demonstrate that using PAR and our proposed deep learning model enables accurate estimation of patient head motion and effectively reduces motion artifacts in the resulting head CT images.

How to solve novel problems: the role of associative learning in problem-solving performance in wild great tits Parus major.

Although problem-solving tasks are frequently used to assess innovative ability, the extent to which problem-solving performance reflects variation in cognitive skills has been rarely formally investigated. Using wild breeding great tits facing a new non-food motivated problem-solving task, we investigated the role of associative learning in finding the solution, compared to multiple other non-cognitive factors. We first examined the role of accuracy (the proportion of contacts made with the opening part of a string-pulling task), neophobia, exploration, activity, age, sex, body condition and participation time on the ability to solve the task. To highlight the effect of associative learning, we then compared accuracy between solvers and non-solvers, before and after the first cue to the solution (i.e., the first time they pulled the string opening the door). We finally compared accuracy over consecutive entrances for solvers. Using 884 observations from 788 great tits tested from 2010 to 2015, we showed that, prior to initial successful entrance, solvers were more accurate and more explorative than non-solvers, and that females were more likely to solve the task than males. The accuracy of solvers, but not of non-solvers, increased significantly after they had the opportunity to associate string pulling with the movement of the door, giving them a first cue to the task solution. The accuracy of solvers also increased over successive entrances. Our results demonstrate that variations in problem-solving performance primarily reflect inherent individual differences in associative learning, and are also to a lesser extent shaped by sex and exploratory behaviour.

MAM-E: Mammographic Synthetic Image Generation with Diffusion Models.

Generative models are used as an alternative data augmentation technique to alleviate the data scarcity problem faced in the medical imaging field. Diffusion models have gathered special attention due to their innovative generation approach, the high quality of the generated images, and their relatively less complex training process compared with Generative Adversarial Networks. Still, the implementation of such models in the medical domain remains at an early stage. In this work, we propose exploring the use of diffusion models for the generation of high-quality, full-field digital mammograms using state-of-the-art conditional diffusion pipelines. Additionally, we propose using stable diffusion models for the inpainting of synthetic mass-like lesions on healthy mammograms. We introduce MAM-E, a pipeline of generative models for high-quality mammography synthesis controlled by a text prompt and capable of generating synthetic mass-like lesions on specific regions of the breast. Finally, we provide quantitative and qualitative assessment of the generated images and easy-to-use graphical user interfaces for mammography synthesis.

Development of Paper-Based Fluorescent Molecularly Imprinted Polymer Sensor for Rapid Detection of Lumpy Skin Disease Virus.

Lumpy Skin Disease (LSD) is a notifiable viral disease caused by Lumpy Skin Disease virus (LSDV). It is usually associated with high economic losses, including a loss of productivity, infertility, and death. LSDV shares genetic and antigenic similarities with Sheep pox virus (SPV) and Goat pox (GPV) virus. Hence, the LSDV traditional diagnostic tools faced many limitations regarding sensitivity, specificity, and cross-reactivity. Herein, we fabricated a paper-based turn-on fluorescent Molecularly Imprinted Polymer (MIP) sensor for the rapid detection of LSDV. The LSDV-MIPs sensor showed strong fluorescent intensity signal enhancement in response to the presence of the virus within minutes. Our sensor showed a limit of detection of 101 log10 TCID50/mL. Moreover, it showed significantly higher specificity to LSDV relative to other viruses, especially SPV. To our knowledge, this is the first record of a paper-based rapid detection test for LSDV depending on fluorescent turn-on behavior.