Anatomical Structures Research Articles

Anatomic Subunit Cleft Lip Repair: New Repair Concept Based on Nostril Sill and Philtrum-White Roll Junction.

A novel method for unilateral cleft lip repair based on lip anatomic subunits was presented in 2005 by David M. Fisher. It has about 25 landmarks with 2 arbitrary markings for the nostril. It is an important subunit when it comes to nose reconstruction. This article sheds light on the variance in nostril sill disposition among cleft lip patients. A novel marking technique based on differences in sills is presented. Furthermore, the unique anatomical structure of the philtrum-white roll junction is highlighted with a suggested modification to the subunit approximation leading to its reconstitution.

The Application of Mask Region-Based Convolutional Neural Networks in the Detection of Nasal Septal Deviation Using Cone Beam Computed Tomography Images: Proof-of-Concept Study.

Artificial intelligence (AI) models are being increasingly studied for the detection of variations and pathologies in different imaging modalities. Nasal septal deviation (NSD) is an important anatomical structure with clinical implications. However, AI-based radiographic detection of NSD has not yet been studied. This research aimed to develop and evaluate a real-time model that can detect probable NSD using cone beam computed tomography (CBCT) images. Coronal section images were obtained from 204 full-volume CBCT scans. The scans were classified as normal and deviated by 2 maxillofacial radiologists. The images were then used to train and test the AI model. Mask region-based convolutional neural networks (Mask R-CNNs) comprising 3 different backbones-ResNet50, ResNet101, and MobileNet-were used to detect deviated nasal septum in 204 CBCT images. To further improve the detection, an image preprocessing technique (contrast enhancement [CEH]) was added. The best-performing model-CEH-ResNet101-achieved a mean average precision of 0.911, with an area under the curve of 0.921. The performance of the model shows that the model is capable of detecting nasal septal deviation. Future research in this field should focus on additional preprocessing of images and detection of NSD based on multiple planes using 3D images.

Coronary Artery Aneurysm or Ectasia as a Form of Coronary Artery Remodeling: Etiology, Pathogenesis, Diagnostics, Complications, and Treatment

Coronary artery aneurysm or ectasia (CAAE) is a term that includes both coronary artery ectasia (CAE) and coronary artery aneurysm (CAA), despite distinct phenotypes and definitions. This anomaly can be found in 0.15–5.3% of coronary angiography. CAE is a diffuse dilatation of the coronary artery at least 1.5 times wider than the diameter of the normal coronary artery in a patient with a length of over 20 mm or greater than one-third of the vessel. CAE can be further subdivided into diffuse and focal dilations by the number and the length of the dilated vessels. Histologically, it presents with extensive destruction of musculoelastic elements, marked degradation of collagen and elastic fibers, and disruption of the elastic lamina. Conversely, CAA is a focal lesion manifesting as focal dilatation, which can be fusiform (if the longitudinal diameter is greater than the transverse) or saccular (if the longitudinal diameter is smaller than the transverse). Giant CAA is defined as a 4-fold enlargement of the vessel diameter and is observed in only 0.02% of patients after coronary. An aneurysmal lesion can be either single or multiple. It can be either a congenital or acquired phenomenon. The pathophysiological mechanisms responsible for the formation of CAAE are not well understood. Atherosclerosis is the most common etiology of CAAE in adults, while Kawasaki disease is the most common in children. Other etiological factors include systemic connective tissue diseases, infectious diseases, vasculitis, congenital anomalies, genetic factors, and idiopathic CAA. Invasive assessment of CAAE is based on coronary angiography. Coronary computed tomography (CT) is a noninvasive method that enables accurate evaluation of aneurysm size and location. The most common complications are coronary spasm, local thrombosis, distal embolization, coronary artery rupture, and compression of adjacent structures by giant coronary aneurysms. The approach to each patient with CAAE should depend on the severity of symptoms, anatomical structure, size, and location of the aneurysm. Treatment methods should be carefully considered to avoid possible complications of CAAE. Simultaneously, we should not unnecessarily expose the patient to the risk of intervention or surgical treatment. Patients can be offered conservative or invasive treatment. However, there are still numerous controversies and ambiguities regarding the etiology, prognosis, and treatment of patients with coronary artery aneurysms. This study summarizes the current knowledge about this disease’s etiology, pathogenesis, and management.

Research progress on the role and mechanism of circular RNA in drug resistance of head and neck squamous cell carcinoma

Drug resistance in tumors constitutes a significant obstacle to tumor therapy. Head and neck squamous cell carcinoma (HNSCC) presents a major challenge due to its deep anatomical location, limited space, and complex structure. These factors complicate surgical procedures and hinder the effectiveness of chemoradiotherapy, leading to poor prognosis and reduced quality of life. However, there is hope in the form of circular RNAs (circRNAs), non-coding RNA molecules with a closed-loop structure that exhibits superior stability and resistance to degradation compared to linear RNAs. Recent advances in high-throughput sequencing and bioinformatics technology revealed that circRNAs participate in tumor proliferation, invasion, migration, and drug resistance. This review aims to summarize current research progress on the involvement of circRNAs in drug resistance of HNSCC and provide valuable insights for the prevention and mitigation of drug resistance in HNSCC.

Variants in the superior attachment of uncinated process and their corresponding frontal sinus drainage pattern: a computed tomography-based retrospective clinical study

Background: Understanding the morphology and spatial relationships of the uncinate process and its associated spaces is crucial for diagnosing and treating conditions affecting sinus drainage and ventilation. This consistent anatomical structure is an important surgical landmark for endoscopic sinus surgery. Methods: Computed tomography (CT) records of 100 patients over two years were extracted via the hospital's PACS system. The type of superior insertion of uncinate process (UP) was observed in coronal cuts of CT scan images, and classification was done based on the Friedmann and Landsberg classification system and tabulated. The corresponding variations in the frontal sinus outflow tract were noticed, and their effect on frontal sinusitis was recorded. Sinusitis of the frontal sinus was in guidance with EPOS 2020 (mucosal changes within the ostiomeatal complex or sinus). Results: Type 2 was the most common of uncinate attachments (39.5%). The least common variant was type 5 (4%). Medial-type frontal sinus drainage pathway was observed in 78.5% of patients. Sinusitis was present in 23.5% of our study population. Type 2 had a 25% contribution to the occurrence of sinusitis. The medial frontal sinus drainage pathway contributes 24% to sinusitis with a significant p value of 0.010. Conclusions: Uncinate process, with its varying superior attachment, influences the frontal sinus outflow tract and may contribute to the development of frontal sinusitis. Awareness of the possible variations in the attachment of the uncinate process is vital in the preoperative planning process, which will serve as a road map to surgeons and aid in preparedness for intraoperative complications.

Protocolo de recolecta y tinción para el estudio taxonómico de los policládidos (Platyhelminthes: Polycladida)

The taxonomic study of polyclads (order Polycladida) dates back to the 19th century. The traditional taxonomy of these organisms requires a combination of the external and internal morphology of the body and the hermaphroditic reproductive system, where the histological techni que is an indispensable tool that plays a key role in this process. This protocol covers the collection processes up to the preparation of the specimens for histological analysis,adapting and improving techniques previously established by other authors for clear and precise visualization of the anatomical structures. Specimens are collected manually using fine paintbrush, photographed dorsally and ventrally, fixed in 10% frozen formalin neutralized with sodium borate and preserved in 70° ethanol. For taxonomic classification, complete mounts of the organisms and sagittal sections of the hermaphroditic reproductive system are used; a five-step histological protocol including hydration, staining, dehydration/differentiation, cleaning/transparency, and slide mounting is performed for each. This standardized technique complements morphological and taxonomic studies of the taxon.

Investigation on performance of multiple AI-based auto-contouring systems in organs at risks (OARs) delineation.

Manual contouring of organs at risk (OAR) is time-consuming and subject to inter-observer variability. AI-based auto-contouring is proposed as a solution to these problems if it can produce clinically acceptable results. This study investigated the performance of multiple AI-based auto-contouring systems in different OAR segmentations. The auto-contouring was performed using seven different AI-based segmentation systems (Radiotherapy AI, Limbus AI version 1.5 and 1.6, Therapanacea, MIM, Siemens AI-Rad Companion and RadFormation) on a total of 42 clinical cases with varying anatomical sites. Volumetric and surface dice similarity coefficients and maximum Hausdorff distance (HD) between the expert's contours and automated contours were calculated to evaluate their performance. Radiotherapy AI has shown better performance than other software in most tested structures considered in the head and neck, and brain cases. No specific software had shown overall superior performance over other software in lung, breast, pelvis and abdomen cases. Each tested AI system was able to produce comparable contours to the experts' contours of organs at risk which can potentially be used for clinical use. A reduced performance of AI systems in the case of small and complex anatomical structures was found and reported, showing that it is still essential to review each contour produced by AI systems for clinical uses. This study has also demonstrated a method of comparing contouring software options which could be replicated in clinics or used for ongoing quality assurance of purchased systems.

3D Printing for Traumatic Orthopedics: A Review of Current Trends and Opportunities

Three-dimensional (3D) printing enables the fabrication of functional scaffolding structures, prototypes, and implants with complex geometries suitable for various biomedical applications. As 3D printing continues to grow, there has been an increasing trend in reshaping the future of biomedical implants and prototypes in orthopedics, particularly in traumatic orthopedics. For example, 3D printing can be employed to create customized implant guides and prostheses to match the anatomical shape and structure of the defective part of the patient and resolve issues associated with clinical treatment. In addition, 3D printing can be used to print patient-specific prototypes, which is helpful for physicians to have a better 3D Printing for Traumatic Orthopedics: understanding of the anatomy and pAreRoepveierawtivoef Cduiarrgennot sTirsenodfsthaneddOepfepcotrttuonibtieesfixed. Keeping these points in viHewan, tehtisal. article reviews current trends and opportunities for 3D printing in traumatic orthopedics.

Enhanced diagnostic accuracy for multiple lung diseases using a fine-tuned MobileNetV2 model with advanced pre-processing techniques

Early diagnosis of lung conditions is crucial for effective treatment and improving patient health. However, traditional diagnostic methods using chest X-ray images have some notable drawbacks, such as overlapping anatomical structures obscuring areas of interest, the presence of noise potentially masking abnormalities, and low contrast diminishing differentiation. In this research undertaking, we explored an enhanced MobileNetV2 approach to augmenting the accuracy of diagnosing multiple concomitant lung pathologies. We employed an inclusive methodology, incorporating several progressive steps. We leveraged contrast-limited adaptive histogram equalization to heighten the clarity of the dataset’s images. Bilateral filtering was applied to refine contrast and sharpness, along with utilizing a dense convolutional neural network. Additional techniques were utilized as well, such as image standardization, Gaussian blur, and histogram equalization, to further increase contrast and reduce noise. This was performed through rotations, scaling, horizontal flipping, brightness adjustment, elastic transformations, and random cropping and padding to generate more realistic exemplars. We defined and compiled a purpose-specific MobileNetV2 architecture for this research endeavor. The model was evaluated based on several metrics, including accuracy, precision, recall, F1-score, and specificity, after every epoch, to refine the training process. We used Bayesian optimization for more efficient fine-tuning of the model. Conditional random fields and ensemble averaging methods were employed for postprocessing. These enhancements led to superior results. After image enhancement, the model achieved an accuracy of 99.20 %, with a precision of 98.90 %, a recall of 99.10 %, and an F1-score of 99.00 %. The specificity of the predictions reached 99.40 %. These improvements assist medical workers in making more informed decisions. However, it is important to note that the current algorithm does not influence the effectiveness of treatment or recovery from the condition

Employing a synergistic bioinformatics and machine learning framework to elucidate biomarkers associating asthma with pyrimidine metabolism genes.

Asthma, a prevalent chronic inflammatory disorder, is shaped by a multifaceted interplay between genetic susceptibilities and environmental exposures. Despite strides in deciphering its pathophysiological landscape, the intricate molecular underpinnings of asthma remain elusive. The focus has increasingly shifted toward the metabolic aberrations accompanying asthma, particularly within the domain of pyrimidine metabolism (PyM)-a critical pathway in nucleotide synthesis and degradation. While the therapeutic relevance of PyM has been recognized across various diseases, its specific contributions to asthma pathology are yet underexplored. This study employs sophisticated bioinformatics approaches to delineate and confirm the involvement of PyM genes (PyMGs) in asthma, aiming to bridge this significant gap in knowledge. Employing cutting-edge bioinformatics techniques, this research aimed to elucidate the role of PyMGs in asthma. We conducted a detailed examination of 31 PyMGs to assess their differential expression. Through Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA), we explored the biological functions and pathways linked to these genes. We utilized Lasso regression and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) to pinpoint critical hub genes and to ascertain the diagnostic accuracy of eight PyMGs in distinguishing asthma, complemented by an extensive correlation study with the clinical features of the disease. Validation of the gene expressions was performed using datasets GSE76262 and GSE147878. Our analyses revealed that eleven PyMGs-DHODH, UMPS, NME7, NME1, POLR2B, POLR3B, POLR1C, POLE, ENPP3, RRM2B, TK2-are significantly associated with asthma. These genes play crucial roles in essential biological processes such as RNA splicing, anatomical structure maintenance, and metabolic processes involving purine compounds. This investigation identifies eleven PyMGs at the core of asthma's pathogenesis, establishing them as potential biomarkers for this disease. Our findings enhance the understanding of asthma's molecular mechanisms and open new avenues for improving diagnostics, monitoring, and progression evaluation. By providing new insights into non-cancerous pathologies, our work introduces a novel perspective and sets the stage for further studies in this field.

Automatic segmentation of echocardiographic images using a Shifted Windows Vision Transformer architecture.

Echocardiography is one the most commonly used imaging modalities for the diagnosis
of congenital heart disease. Echocardiographic image analysis is crucial to obtaining
accurate cardiac anatomy information. Semantic segmentation models can be used
to precisely delimit the borders of the left ventricle, and allow an accurate and
automatic identification of the region of interest, which can be extremely useful for
cardiologists. In the field of computer vision, convolutional neural network (CNN)
architectures remain dominant. Existing CNN approaches have proved highly efficient
for the segmentation of various medical images over the past decade. However, these
solutions usually struggle to capture long-range dependencies, especially when it comes
to images with objects of different scales and complex structures. In this study, we
present an efficient method for semantic segmentation of echocardiographic images
that overcomes these challenges by leveraging the self-attention mechanism of the
Transformer architecture. The proposed solution extracts long-range dependencies and
efficiently processes objects at different scales, improving performance in a variety of
tasks. We introduce Shifted Windows Transformer models (Swin Transformers), which
encode both the content of anatomical structures and the relationship between them.
Our solution combines the Swin Transformer and U-Net architectures, producing a
U-shaped variant. The validation of the proposed method is performed with the
EchoNet-Dynamic dataset used to train our model. The results show an accuracy
of 0.97, a Dice coefficient of 0.87, and an Intersection over union (IoU) of 0.78.
Swin Transformer models are promising for semantically segmenting echocardiographic
images and may help assist cardiologists in automatically analyzing and measuring
complex echocardiographic images.

Septic shock in the immunocompromised cancer patient: a narrative review.

Immunosuppressed patients, particularly those with cancer, represent a momentous and increasing portion of the population, especially as cancer incidence rises with population growth and aging. These patients are at a heightened risk of developing severe infections, including sepsis and septic shock, due to multiple immunologic defects such as neutropenia, lymphopenia, and T and B-cell impairment. The diverse and complex nature of these immunologic profiles, compounded by the concomitant use of immunosuppressive therapies (e.g., corticosteroids, cytotoxic drugs, and immunotherapy), superimposed by the breakage of natural protective barriers (e.g., mucosal damage, chronic indwelling catheters, and alterations of anatomical structures), increases the risk of various infections. These and other conditions that mimic sepsis pose substantial diagnostic and therapeutic challenges. Factors that elevate the risk of progression to septic shock in these patients include advanced age, pre-existing comorbidities, frailty, type of cancer, the severity of immunosuppression, hypoalbuminemia, hypophosphatemia, Gram-negative bacteremia, and type and timing of responses to initial treatment. The management of vulnerable cancer patients with sepsis or septic shock varies due to biased clinical practices that may result in delayed access to intensive care and worse outcomes. While septic shock is typically associated with poor outcomes in patients with malignancies, survival has significantly improved over time. Therefore, understanding and addressing the unique needs of cancer patients through a new paradigm, which includes the integration of innovative technologies into our healthcare system (e.g., wireless technologies, medical informatics, precision medicine), targeted management strategies, and robust clinical practices, including early identification and diagnosis, coupled with prompt admission to high-level care facilities that promote a multidisciplinary approach, is crucial for improving their prognosis and overall survival rates.

Aspects of renal function and renal artery anatomy as indications for renal denervation.

Renal denervation (RDN) is a minimally invasive, endovascular catheter-based procedure using radiofrequency, ultrasound, or alcohol-mediated ablation to treat resistant hypertension. As more attention is focused on the renal sympathetic nerve as a cause and treatment target of hypertension, understanding the anatomy of the renal artery may have important implications for determining endovascular treatment strategies as well as for future selection of devices and appropriate candidates for RDN treatment. However, the anatomical structure of the renal artery (RA) is complex, and standardized morphological evaluations of the RA structure are lacking. Computed tomography angiography or magnetic resonance angiography imaging is useful for assessing RA anatomy before conducting RDN. RA echocardiography is an established noninvasive screening method for significant stenosis. Major randomized controlled trials have limited enrollment to patients with preserved renal function, usually defined as an estimated glomerular filtration rate (eGFR) ≥ 45 mL/min/1.73 m2. Therefore, the level of renal function at which RDN is indicated has not yet been determined. This mini-review summarizes the characteristics of renal artery anatomy and renal function that constitute indications for renal denervation. (Role of Clinical Trials: K. Kario is an Executive Committee Principal Investigator for the Spyral OFF MED, the Spyral ON MED, the DUO and the REQUIRE; a Coordinating investigator for the TCD-16164 study; a Site Principal Investigator for the HTN-J, the Spyral OFF MED, the Spyral ON MED, the DUO, the REQUIRE and the TCD-16164 study). Evaluation of renal arteries for radiofrequency renal denervation. A Simultaneous quadrantal ablations at four sites in the main renal artery or the equivalent renal artery to the main renal artery. B If there is a renal artery branch with a diameter >3 mm in the middle of the main renal artery, this branch is the distal end of the main renal artery. In this case, four simultaneous and quadrantal ablations can be performed on the equivalent renal arteries. C Four simultaneous and quadrantal ablations can be performed in the branch renal artery. D Sonication should be spaced at least 5 mm (one transducer*) apart. Perform 2 to 3 mm proximal to the arterial bifurcation. Perform 2 to 3 mm distal to the abdominal aortic inlet.

Heracleum munzurense (Apiaceae), a new species from East Anatolia, Turkey and taxonomic position of Tetrataenium lasiopetalum

A new species, Heracleum munzurense (Apiaceae), is described from East Anatolia, Turkey. It grows in damp places formation of the Munzur Mountains in the district of Pülümür (Tunceli Province). The taxonomic affinities of the new species were determined by analyzing morphological data and through a molecular phylogenetic study of nrITS/ETS. The new species is morphologically and genetically related to H. apiifolium and H. pastinaca, but differs mainly in stems, leaves, pedicels, rays and petals pubescence, petals size, fruit hairs, and size features. The anatomical structure of the fruit is given. The diagnostic morphological characters of the new species are discussed. In this study, the geographical distribution of the new species and the related taxa H. apiifolium, H. lasiopetalum, and H. pastinaca is mapped. In addition, the habitat, ecology, phenology, and conservation status of the new species are also provided. Tetrataenium lasiopetalum is also closely related to this group of species. Common morphological features and results of molecular phylogenetic study suggest to transfer this species back to the genus Heracleum.

Systemic organoprotection with inhaled nitric oxide (literature review)

Sufficient levels of nitric oxide (NO) ensure adequate blood flow to all organs and tissues. Despite the contradictory data on the role of endogenous NO as an organоprotector, NO insufflation is a promising direction, which is supported by evidence of modeling the protective effect on the myocardium, kidneys, and liver with exogenous NO in experimental and clinical studies. The largest number of studies have been conducted on models of ischemia-reperfusion injury in cardiovascular surgery. There are very few studies in abdominal and other non-cardiac surgery and they are mostly experimental. This review describes possible ways of implementing the organоprotective effect of NO, however, the exact mechanism remains not fully understood. One of the main links in the development of abdominal organ injury is intra-abdominal hypertension (IAH), which always accompanies laparoscopic surgeries and can last up to several hours. IAH causes ischemia of the kidneys and gastrointestinal mucosa with possible subsequent development of organ dysfunction. The degree of damage will depend not only on the duration of IAH but also on the patient’s premorbid background. The prognosis will be especially aggravated by the presence of atherosclerotic vascular lesions, which creates a preoperative background for hypoperfusion of visceral organs, which, due to the characteristics of vascularization, anatomical structure, and functioning, are very sensitive to the slightest disturbances in perfusion pressure and to the systemic inflammatory reaction, which will subsequently lead to an increase in vascular permeability, the formation of transcapillary leakage and interstitial edema, which is the beginning of organ dysfunction. Dysregulation of the mechanisms involved in NO production may be a link in the pathogenesis of the development of organ’s dysfunction, so maintaining adequate NO levels may be a target for therapy.

Floral structure and nectary anatomy of Bertholletia excelsa (Lecythidaceae)

ABSTRACT The floral nectary of Bertholletia excelsa was studied through permanent, histological cross-sections. The flower showed two rings of androecial vascular bundles, the inner for the fertile stamens and the outer for the androecial hood. The androecial hood, originating from the abaxial side of the androecium, is a well-developed, curved, and highly unilateral structure that covers the fertile stamens and stigma. At the apex and on the inner surface of this structure, several finger-like appendages. Histological cross-sections of the androecial hood, from the distal part towards the centre of the flower, reveal that the secretory parenchyma originates in the medium of the inner-distal part of the hood. The secretory tissue is surrounded by vascular bundles that more distally supply the finger-like appendages of the androecial hood. The nectariferous parenchyma lacks internal vascular bundles and consists of small cells, starch-rich, with large vacuoles and a prominent nucleus. The nectariferous epidermis is striated. No stomata or glandular hairs were observed associated with the nectariferous epidermis. The nectary is located within the distal part of the androecial hood and can be classified as a structured, non-vascularized, androecial type. This specialization in the location of the nectar secreting tissue is linked with a complex pollination mechanism, allowing access to the reward only for large bees with long proboscis.

Cross-modal Transfer Learning Based on an Improved CycleGAN Model for Accurate Kidney Segmentation in Ultrasound Images

ObjectiveDeep-learning algorithms have been widely applied in the field of automatic kidney ultrasound (US) image segmentation. However, obtaining a large number of accurate kidney labels clinically is very difficult and time-consuming. To solve this problem, we have proposed an efficient cross-modal transfer learning method to improve the performance of the segmentation network on a limited labeled kidney US dataset. MethodsWe aim to implement an improved image-to-image translation network called Seg-CycleGAN to generate accurate annotated kidney US data from labeled abdomen computed tomography images. The Seg-CycleGAN framework primarily consists of two structures: (i) a standard CycleGAN network to visually simulate kidney US from a publicly available labeled abdomen computed tomography dataset; (ii) and a segmentation network to ensure accurate kidney anatomical structures in US images. Based on the large number of simulated kidney US images and small number of real annotated kidney US images, we then aimed to employ a fine-tuning strategy to obtain better segmentation results. ResultsTo validate the effectiveness of the proposed method, we tested this method on both normal and abnormal kidney US images. The experimental results showed that the proposed method achieved a segmentation accuracy of 0.8548 in dice similarity coefficient on all testing datasets and 0.7622 on the abnormal testing dataset. ConclusionsCompared with existing data augmentation and transfer learning methods, the proposed method improved the accuracy and generalization of the kidney US image segmentation network on a limited number of training datasets. It therefore has the potential to significantly reduce annotation costs in clinical settings.

Effect of the cone-beam CT acquisition trajectory on image quality in spine surgery: experimental cadaver study

Background: Intraoperative 3D imaging with cone-beam CT (CBCT) improves assessment of implant position and reduces complications in spine surgery. It is also used for image-guided surgical techniques, resulting in improved quality of care. However, in some cases, metal artifacts can reduce image quality and make it difficult to assess pedicle screw position and reduction. Purpose: The objective of this study was to investigate whether a change in CBCT acquisition trajectory in relation to pedicle screw position during dorsal instrumentation can reduce metal artifacts and consequently improve image quality and clinical assessability. Study design: Experimental cadaver study Methods: A human cadaver was instrumented with pedicle screws in the thoracic and lumbar spine region (Th11 to L5). Then, the acquisition trajectory of the CBCT (Cios Spin, Siemens, Germany) to the pedicle screws was systematically changed in 5° steps in angulation (-30° to +30°) and swivel (-25° to +25°). Subsequently, radiological evaluation was performed by three blinded, qualified raters on image quality using 9 questions (including anatomical structures, implant position, appearance of artifacts) with a score (1-5 points). For statistical evaluation, the image quality of the different acquisition trajectories was compared to the standard acquisition trajectory and checked for significant differences. Results: The angulated acquisition trajectory significantly increased the score for subjective image quality (p<0.001) as well as the clinical assessability of pedicle screw position (p<0.001) with particularly strong effects on subjective image quality in the vertebral pedicle region (d=1.61). Swivel of the acquisition trajectory significantly improved all queried domains of subjective image quality (p<0.001) as well as clinical assessability of pedicle screw position (p<0.001). Conclusions: In this cadaver study, the angulation as well as the swivel of the acquisition trajectory led to a significantly improved image quality in intraoperative 3D imaging (CBCT) with a constant isocenter. The data show that maximizing the angulation/swivel angle towards 30°/25° provides the best tested subjective image quality and enhances clinical assessability. Therefore, a correct adjustment of the acquisition trajectory can help to make intraoperative revision decisions more reliably. Clinical significance: The knowledge of enhanced image quality by changing the acquisition trajectory in intraoperative 3D imaging can be used for the assessment of critical screw positions in spine surgery. The implementation of this knowledge requires only a minor change of the current intraoperative imaging workflow without additional technical equipment and could further reduce the need for revision surgery.

Estimating the severity of obstructive sleep apnea during wakefulness using speech: A review

Obstructive sleep apnea (OSA) is a chronic breathing disorder during sleep that affects 10-30% of adults in North America. The gold standard for diagnosing OSA is polysomnography (PSG). However, PSG has several drawbacks, for example, it is a cumbersome and expensive procedure, which can be quite inconvenient for patients. Additionally, patients often have to endure long waitlists before they can undergo PSG. As a result, other alternatives for screening OSA have gained attention. Speech, as an accessible modality, is generated by variations in the pharyngeal airway, vocal tract, and soft tissues in the pharynx, which shares similar anatomical structures that contribute to OSA. Consequently, in this study, we aim to provide a comprehensive review of the existing research on the use of speech for estimating the severity of OSA. In this regard, a total of 851 papers were initially identified from the PubMed database using a specified set of keywords defined by population, intervention, comparison and outcome (PICO) criteria, along with a concatenated graph of the 5 most cited papers in the field extracted from ConnectedPapers platform. Following a rigorous filtering process that considered the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach, 32 papers were ultimately included in this review. Among these, 28 papers primarily focused on developing methodology, while the remaining 4 papers delved into the clinical perspective of the association between OSA and speech. In the next step, we investigate the physiological similarities between OSA and speech. Subsequently, we highlight the features extracted from speech, the employed feature selection techniques, and the details of the developed models to predict OSA severity. By thoroughly discussing the current findings and limitations of studies in the field, we provide valuable insights into the gaps that need to be addressed in future research directions.

DAW-FA: Domain-aware adaptive weighting with fine-grain attention for unsupervised MRI harmonization

Magnetic resonance (MR) imaging often lacks standardized acquisition protocols across various sites, leading to contrast variations that reduce image quality and hinder automated analysis. MR harmonization improves consistency by integrating data from multiple sources, ensuring reproducible analysis. Recent advances leverage image-to-image translation and disentangled representation learning to decompose anatomical and contrast representations, achieving consistent cross-site harmonization. However, these methods face two significant drawbacks: imbalanced contrast availability during training affects adaptation performance, and insufficient utilization of spatial variability in local anatomical structures limits model adaptability to different sites. To address these challenges, we propose Domain-aware Adaptive Weighting with Fine-Grain Attention (DAW-FA) for Unsupervised MRI Harmonization. DAW-FA incorporates an adaptive weighting mechanism and enhanced self-attention to mitigate MR contrast imbalance during training and account for spatial variability in local anatomical structures. This facilitates robust cross-site harmonization without requiring paired inter-site images. We evaluated DAW-FA on MR datasets with varying scanners and acquisition protocols. Experimental results show DAW-FA outperforms existing methods, with an average increase of 1.92 ± 0.56 in Peak Signal-to-Noise Ratio (PSNR) and 0.023 ± 0.011 in Structural Similarity Index Measure (SSIM). Additionally, we demonstrate DAW-FA’s impact on downstream tasks: Alzheimer’s disease classification and whole-brain segmentation, highlighting its potential clinical relevance.