Identification Of Landmarks Research Articles

A new approach to peripheral nerve block education with the Anatomage Table as a learning adjunct.

Human anatomy education serves as a gateway for entering the intricacies of health science. Human cadavers have been the gold standard for learning regional and gross anatomy. However, increasing barriers in acquisition, maintenance, and longevity have pushed anatomy education toward technology-based alternatives such as the Anatomage Table (AT), an interactive, life-sized virtual dissection table with many anatomy education-centric features. The AT has found purchase in various contexts, such as clinical settings, research, outreach, and education. Studies into the efficacy of the AT in teaching settings have been generally positive but limited in its application, particularly in clinical procedure education. In this study, we conducted an informal workshop for second-year Certified Registered Nurse Anesthetist (CRNA) students to aid in being able to identify the important neuraxial landmarks for performing peripheral nerve blocks (PNBs), an anesthetic technique often used before other procedures. In our workshop, we paired the AT with identification of the same neuraxial landmarks on volunteer models with an ultrasound probe to provide students with relevant tactile experience for the procedure. From our pre-/post-surveys of the participants (n = 29), we found that our workshop significantly increased student confidence in identifying the relevant neuraxial landmarks for and in performing PNBs. Our results support the use of the AT in clinical education as a supplement, particularly where other anatomic teaching tools, such as cadaver models, may be too difficult to implement.NEW & NOTEWORTHY We implemented the Anatomage Table (AT) and portable ultrasound to teach neuraxial landmarks for performing peripheral nerve blocks (PNB), an anesthetic technique for Certified Registered Nurse Anesthetist (CRNA) students. The workshop significantly increased student confidence in identifying the relevant neuraxial landmarks for performing PNBs. Our results support the use of the AT in clinical education as a supplement.

The fork sign: a new cortical landmark in the human brain

Abstract In the cerebral cortex, establishing the precise relationship between functional areas and the macroscopic anatomy of gyri and sulci has a paramount importance for the field of neuroimaging and neurosurgical interventions. The anatomical orientation should start with the identification of anatomical landmarks to set the anatomo-functional boundaries. The human central sulcus region stands out as a well-defined structural and functional unit housing the primary motor and sensory cortices and is considered as key region to be identified during brain surgery. While useful anatomical landmarks have been discovered, especially in the axial plane, the identification of this region in the sagittal plane remains sometimes difficult. Using cadaveric whole brains and multimodal analysis of MRI brain scans, we systematically observed a tuning fork shaped sulco-gyral configuration centered around the gyral continuum bridging the precentral gyrus with the middle frontal gyrus. We provide evidence that this “Fork sign” is a consistent morphological feature visible on the lateral surface of the brain and a reliable radioanatomical landmark for identifying central sulcus region structures on sagittal MRI images, including the motor hand area.

Integrating AI into Breast Reconstruction Surgery: Exploring Opportunities, Applications, and Challenges.

Background: Artificial intelligence (AI) has significantly influenced various sectors, including healthcare, by enhancing machine capabilities in assisting with human tasks. In surgical fields, where precision and timely decision-making are crucial, AI's integration could revolutionize clinical quality and health resource optimization. This study explores the current and future applications of AI technologies in reconstructive breast surgery, aiming for broader implementation. Methods: We conducted systematic reviews through PubMed, Web of Science, and Google Scholar using relevant keywords and MeSH terms. The focus was on the main AI subdisciplines: machine learning, computer vision, natural language processing, and robotics. This review includes studies discussing AI applications across preoperative, intraoperative, postoperative, and academic settings in breast plastic surgery. Results: AI is currently utilized preoperatively to predict surgical risks and outcomes, enhancing patient counseling and informed consent processes. During surgery, AI supports the identification of anatomical landmarks and dissection strategies and provides 3-dimensional visualizations. Robotic applications are promising for procedures like microsurgical anastomoses, flap harvesting, and dermal matrix anchoring. Postoperatively, AI predicts discharge times and customizes follow-up schedules, which improves resource allocation and patient management at home. Academically, AI offers personalized training feedback to surgical trainees and aids research in breast reconstruction. Despite these advancements, concerns regarding privacy, costs, and operational efficacy persist and are critically examined in this review. Conclusions: The application of AI in breast plastic and reconstructive surgery presents substantial benefits and diverse potentials. However, much remains to be explored and developed. This study aims to consolidate knowledge and encourage ongoing research and development within the field, thereby empowering the plastic surgery community to leverage AI technologies effectively and responsibly for advancing breast reconstruction surgery.

Accuracy of web-based automated versus digital manual cephalometric landmark identification.

The purpose of this study was to assess the accuracy of two web-based automated cephalometric landmark identification and analysis programs. Manual landmark identification using Dolphin Imaging software was used as reference. 105 cephalograms were selected and divided into three groups of 35 subjects each, Class I, II and III. Radiographs were traced using Dolphin imaging software. WebCeph™ (South Korea) and Cephio™ (Poland) were used for the automated cephalometric analysis. Bland-Altman limits of agreement and the concordance correlation coefficient (CCC) were calculated. Kruskal Wallis test was used to compare the accuracy of WebCeph™ and Cephio™ measurements between the three groups. Mann-Whitney U test was used to compare the absolute difference between cephalometric measurements obtained using WebCeph™ and Cephio™. The mean difference (MD) between AI and manually-derived measurements was less than 1mm/degree and ranged from 0.01 to 0.8 except for upper lip protrusion (MD 1.35°), nasolabial angle (MD 5.01°), SN-GoGn (MD 1.41°), Ramus height (MD 1.46°), and IMPA (MD 1.94°). The mean CCC was 0.91 (range 0.60 to 0.96). No statistically significant differences were found between the three malocclusion groups for most of the measurements (P > 0.05). For most of the measurements, automated cephalometric measurements were clinically acceptable. Few differences were found between Webceph™ and Cephio™ for most measurements. Measurements including SNA, SN-PP, IMPA as well as soft tissue measurements require extra consideration and manual adjustment of respective landmarks for higher precision and improved efficiency.

Evaluation of automated photograph-cephalogram image integration using artificial intelligence models.

To develop and evaluate an automated method for combining a digital photograph with a lateral cephalogram. A total of 985 digital photographs were collected and soft tissue landmarks were manually detected. Then 2500 lateral cephalograms were collected, and corresponding soft tissue landmarks were manually detected. Using the images and landmark identification information, two different artificial intelligence (AI) models-one for detecting soft tissue on photographs and the other for identifying soft tissue on cephalograms-were developed using different deep-learning algorithms. The digital photographs were rotated, scaled, and shifted to minimize the squared sum of distances between the soft tissue landmarks identified by the two different AI models. As a validation process, eight soft tissue landmarks were selected on digital photographs and lateral cephalometric radiographs from 100 additionally collected validation subjects. Paired t-tests were used to compare the accuracy of measures obtained between the automated and manual image integration methods. The validation results showed statistically significant differences between the automated and manual methods on the upper lip and soft tissue B point. Otherwise, no statistically significant difference was found. Automated photograph-cephalogram image integration using AI models seemed to be as reliable as manual superimposition procedures.

Anatomical landmark detection on bi-planar radiographs for predicting spinopelvic parameters.

Accurate landmark detection is essential for precise analysis of anatomical structures, supporting diagnosis, treatment planning, and monitoring in patients with spinal deformities. Conventional methods rely on laborious landmark identification by medical experts, which motivates automation. The proposed deep learning pipeline processes bi-planar radiographs to determine spinopelvic parameters and Cobb angles without manual supervision. The dataset used for training and evaluation consisted of 555 bi-planar radiographs from un-instrumented patients, which were manually annotated by medical professionals. The pipeline performed a pre-processing step to determine regions of interest, including the cervical spine, thoracolumbar spine, sacrum, and pelvis. For each ROI, a segmentation network was trained to identify vertebral bodies and pelvic landmarks. The U-Net architecture was trained on 455 bi-planar radiographs using binary cross-entropy loss. The post-processing algorithm determined spinal alignment and angular parameters based on the segmentation output. We evaluated the pipeline on a test set of 100 previously unseen bi-planar radiographs, using the mean absolute difference between annotated and predicted landmarks as the performance metric. The spinopelvic parameter predictions of the pipeline were compared to the measurements of two experienced medical professionals using intraclass correlation coefficient(ICC) and mean absolute deviation (MAD). The pipeline was able to successfully predict the Cobb angles in 61% of all test cases and achieved mean absolute differences of 3.3° (3.6°) and averaged ICC of 0.88. For thoracic kyphosis, lumbar lordosis, sagittal vertical axis, sacral slope, pelvic tilt, and pelvic incidence, the pipeline produced reasonable outputs in 69%, 58%, 86%, 85%, 84%, and 84% of the cases. The MAD was 5.6° (7.8°), 4.7° (4.3°), 2.8mm (3.0mm), 4.5° (7.2°), 1.8° (1.8°), and 5.3° (7.7°), while the ICC was measured at 0.69, 0.82, 0.99, 0.61, 0.96, and 0.70, respectively. Despite limitations in patients with severe pathologies and high BMI, the pipeline automatically predicted coronal and sagittal spinopelvic parameters, which has the potential to simplify clinical routines and large-scale retrospective data analysis.

Bones at Home

Remote teaching during the COVID-19 pandemic led to a range of pedagogical challenges for anthropology laboratory courses. In biological anthropology courses such as Human Osteology, hands-on experience is essential to achieving learning outcomes, including basic bone and feature (i.e., landmark) identification, identification from fragmentary remains, and age and sex estimation. To address the need for training that includes object-based, tactile (haptic) learning in fields such as biological anthropology and archaeology, all Human Osteology students at Mount Royal University and the University of Manitoba took home plastic model skeletons. The purpose of this study was to evaluate how well remotely educated undergraduates (REUs) met human osteology learning objectives when supported by plastic model skeletons at home. We present the results of a survey designed to test core osteological skills obtained by REUs in comparison with undergraduates educated with in-person laboratory components (IPUs) and experts in the field (zero to four and five or more years of experience). REU scores did not differ significantly from those of IPU or Junior Experts with less than five years of experience. Students performed well in bone identification but were limited in their ability to apply common sex and age estimation methods and to identify incomplete elements. Our findings reinforce the importance of haptic learning and years of experience in human osteological learning. They support the use of take-home models as valuable resources in both remote and in-person undergraduate teaching. This work is a step toward more inclusive universal instructional design that can be applied across various anthropology laboratory courses.

Comparison of Point-of-Care Ultrasound Guidance and the Traditional Approach in Performing Simulated Emergent Cricothyroidotomy Among Emergency Medicine Physicians, Residents, and Students.

Objective Emergency cricothyroidotomy (EC) is a rare procedure used to establish airway access when both endotracheal intubation and bag-mask ventilation have failed. Point-of-care ultrasound (POCUS) has been proposed as an adjunct to aid in identifying anatomical landmarks. However, its impact on emergency physicians when performing EC remains unclear. This study compared emergency physician and student confidence, preference, and procedure time between POCUS and traditional palpation for EC. Methods Our study utilized a randomized controlled crossover design. Emergency medicine providers attended a didactic lecture demonstrating traditional palpation and POCUS techniques for EC. The participants were randomly allocated to two simulation groups. One group started with the palpation of landmarks, while the other group started with POCUS landmark identification. A randomized crossover design was employed, and in a subsequent simulation, the reverse technique was utilized. Time to anatomic landmark identification was recorded. Participants completed a survey assessing their confidence and preference. Procedure times and confidence level differences were examined via Wilcoxon signed-rank tests. Results Sixteen participants completed the survey and were included in the final analysis. All participants successfully performed EC with the two landmark identification techniques. There was no significant difference in self-reported confidence between POCUS and palpation (p = 0.17). Landmark identification by palpation was significantly faster than POCUS (p = 0.001). A total of 10 (63%) participants preferred palpation over POCUS. Conclusions Both POCUS and traditional palpation were effective in identifying anatomical landmarks for EC. Although palpation was significantly quicker, confidence and preference between the two techniques were similar. The results suggest that both approaches can benefit clinical practice, depending on the context and provider familiarity. Further studies with larger cohorts and real-world scenarios are recommended to explore the effectiveness and safety of POCUS in EC.

POCUS Diagnosis of Deep Neck Space Abscess: A Case Series

BackgroundPara and Retropharyngeal abscesses are deep neck infections of early childhood that can be complicated by serious sequelae such as airway obstruction, cervical necrotizing fasciitis, mediastinitis, aspiration pneumonia, jugular thrombosis or aneurysm of the carotid artery. Traditionally, these infections were diagnosed with CT of the neck, which exposes sensitive structures to radiation and may require sedation. Case ReportWe present a case series of four children diagnosed on POCUS with para and/or retropharyngeal abscess later confirmed on CT. All four had alternative working diagnoses on pediatric emergency physician and/or otolaryngology physical examination prior to investigation with POCUS. We also describe a novel imaging approach that allows for easier identification of deep neck anatomic landmarks."Why should an emergency physician be aware of this?"Pediatric emergency physicians should be skilled in imaging the deep neck spaces in order to avoid delayed diagnosis of deep neck space abscess and its potentially catastrophic sequelae.

PelviNet: A Collaborative Multi-agent Convolutional Network for Enhanced Pelvic Image Registration.

PelviNet introduces a groundbreaking multi-agent convolutional network architecture tailored for enhancing pelvic image registration. This innovative framework leverages shared convolutional layers, enabling synchronized learning among agents and ensuring an exhaustive analysis of intricate 3D pelvic structures. The architecture combines max pooling, parametric ReLU activations, and agent-specific layers to optimize both individual and collective decision-making processes. A communication mechanism efficiently aggregates outputs from these shared layers, enabling agents to make well-informed decisions by harnessing combined intelligence. PelviNet's evaluation centers on both quantitative accuracy metrics and visual representations to elucidate agents' performance in pinpointing optimal landmarks. Empirical results demonstrate PelviNet's superiority over traditional methods, achieving an average image-wise error of 2.8 mm, a subject-wise error of 3.2 mm, and a mean Euclidean distance error of 3.0 mm. These quantitative results highlight the model's efficiency and precision in landmark identification, crucial for medical contexts such as radiation therapy, where exact landmark identification significantly influences treatment outcomes. By reliably identifying critical structures, PelviNet advances pelvic image analysis and offers potential enhancements for broader medical imaging applications, marking a significant step forward in computational healthcare.

Euler-angle norms for tooth rotation, torque and tip.

The aim of the current study was to develop and describe a new measuring system for the orientation of a tooth in a digitalized cast of a jaw and provide new angular values for the rotation, torque and tip of maxillary and mandibular teeth. This retrospective cross-sectional study involved the utilization of a sub-group of extrinsic Euler-angles to derive optimal norm values per tooth in three different planes of orientation ('rotation', 'torque' and 'tip') by evaluating the digital representations of the teeth derived from a database containing over 17,500 patients. The process involved the entry of the .stl files of the jaw pairs into a fully automated software system (Smyl:Ai, Ulm, Germany) whereupon jaw alignment, teeth segmentation, landmark identification and visual validation of input files was conducted prior to calculation of the norm values for the three different planes of orientation. The digital scans in stereolithography (.STL)-file format of the upper and lower dentitions of 1914 individuals with optimal occlusion were chosen and evaluated. New mean (standard deviation) angular values were determined for the rotation, torque and tip of maxillary and mandibular teeth. The findings facilitate the reappraisal of rotation, torque and tip values currently acceptable as ideal. They will inform anthropologists and dental researchers about occlusion and alignment in orthodontic and non-orthodontic patients and provide baseline data for future studies. The methodology will also enable the evaluation of large numbers of data in relatively short timeframes.

The Feasibility of Prenatal Ultrasonic Screening for Developmental Dysplasia of the Hip.

developmental dysplasia of the hip (DDH) is a condition characterized by abnormal hip development in infancy. Early diagnosis allows for effective treatment, while late presentation often necessitates complex surgical interventions. Current recommendations advise screening between the 6th and 8th week postnatal using an ultrasound, typically employing the Graf method. However, there is no universal consensus on whether ultrasound screening significantly increases treatment likelihood compared to clinical examination-guided ultrasound. This study aims to explore the feasibility of prenatal ultrasound for the early identification of DDH risk. This prospective observational study involved 100 pregnant women undergoing fetal hip ultrasounds during the second and third trimesters. Using the modified Graf method, alpha and beta angles were calculated on the fetus. Postnatally, alpha and beta angles were compared with the prenatal values. Prenatal ultrasound at the 24th week showed inconclusive results because of the difficulty in identification of Graf landmarks, while ultrasound at the 34th week proved to be a reliable and safe method for the quantitative determination of alpha and beta angles. Significant correlations were found between prenatal and postnatal alpha and beta angles. Moreover, significant differences in prenatal alpha and beta values were observed in patients developing mature/immature hips postnatally. Prenatal diagnostics show promise for predicting infant hip development. Further research is warranted to validate correlation strength and clinical applicability.

What not to do in facial infrared thermographic measurements: A post data enhancement

The accuracy of infrared thermographic measurements depends on several factors, including movement of target. In this study, accuracy of nose tip temperatures obtained in a mental workload assessment using a thermal imaging camera were impacted by participants’ movement and camera zooming/panning. To correct these measurement errors, we compared manual facial landmark identification techniques using data labelling software with an automated deep learning-based approach utilised for facial landmark tracking and evaluated both against the built-in tracking features of the thermal camera, Thermal Spot Tracking. Using the Manual Thermal Landmark Annotation measurements as the ground truth, our results show that the Automated Facial Feature Tracking approach, which is the AI based approach performed better than the Thermal Spot Tracking as it matched comparatively more spatial coordinates and temperature datapoints as well as showed comparatively lower mean relative error. The study highlights the potential of AI in enhancing the accuracy of thermographic measurements, particularly in applications involving facial temperature analysis.

A two-stage regression framework for automated cephalometric landmark detection incorporating semantically fused anatomical features and multi-head refinement loss

Accurate identification and precise localization of cephalometric landmarks provide clinicians with essential insights into craniofacial deformities, aiding in the assessment of treatment strategies for improved patient outcomes. The current methodologies heavily depend on the utilization of multiple CNNs for predicting landmark coordinates, which makes them computationally burdensome and unsuitable for translation to clinical applications. To overcome this limitation, we propose a novel, end-to-end trainable, two-stage regression framework for cephalometric landmark detection. In the initial stage, a single neural network is employed to estimate the locations of all landmarks simultaneously, enabling the identification of potential landmark regions. In the second stage, a semantic fusion block leverages the in-network multi-resolution feature hierarchy to produce high-level semantically rich features. These feature maps are then cropped based on coarsely detected landmark locations and concurrent refinement loss is used to fine-tune and refine the landmark locations. The proposed framework demonstrates the potential for enhancing clinical workflow and treatment outcomes in orthodontics. This is achieved through the utilization of a single CNN backbone augmented with multi-resolution semantically fused anatomical features, which effectively enhances representation learning in a computationally efficient manner. The performance of the proposed framework is evaluated on two publicly available anatomical landmark datasets. The experimental results demonstrate that our framework achieves a state-of-the-art detection accuracy of 87.17% within the clinically accepted range of 2 mm. The source code and the pre-trained weights are made publicly available at this https://github.com/manwaarkhd/CEPHMark-Net, promoting reproducibility and enabling further advancements.

Artificial intelligence-assisted ultrasound-guided regional anaesthesia: An explorative scoping review.

The present study reviews the available scientific literature on artificial intelligence (AI)-assisted ultrasound-guided regional anaesthesia (UGRA) and evaluates the reported intraprocedural parameters and postprocedural outcomes. A literature search was performed on 19 September2023, using the Medline, EMBASE, CINAHL, Cochrane Library and Google Scholar databases by experts in electronic searching. All study designs were considered with no restrictions regarding patient characteristics or cohort size. Outcomes assessed included the accuracy of AI-model tracking, success at the first attempt, differences in outcomes between AI-assisted and unassisted UGRA, operator feedbackand case-report data. A joint adaptive median binary pattern (JAMBP) has been applied to improve the tracking procedure, while a particle filter (PF) is involved in feature extraction. JAMBP combined with PF was most accurate on all images for landmark identification, with accuracy scores of 0.83, 0.93and 0.93 on original, preprocessedand filtered images, respectively. Evaluation of first-attempt success of spinal needle insertion revealed first-attempt success in most patients. When comparing AI application versus UGRA alone, a significant statistical difference (p < 0.05) was found for correct block view, correct structure identificationand decrease in mean injection time, needle track adjustments and bone encountersin favour of having AI assistance. Assessment of operator feedback revealed that expert and nonexpert operator feedback was overall positive. AI appears promising to enhance UGRA as well as to positively influence operator training. AI application of UGRA may improve the identification of anatomical structures and provide guidance for needle placement, reducing the risk of complications and improving patient outcomes. Level IV.

A systematic review of 3D printing in autologous breast reconstruction

Aim: The use of three-dimensionally printed (3DP), patient-specific models in autologous breast reconstruction is gaining popularity, namely for their benefits in surgical planning and ability to aid in aesthetic outcomes. Furthermore, 3DP patient-specific models serve as a safe alternative to intraoperative surgical training and act as a useful tool for visualizing the intramuscular course of deep inferior epigastric perforator vessels. Despite demonstrated usefulness in other surgical specialties and areas of plastic surgery, there remains a significant gap in the literature exploring specific perioperative, preoperative, and intraoperative uses as well as the educational advantages of 3DP in autologous breast reconstruction. Methods: PubMed, MEDLINE, EMBASE, Scopus, and the Cochrane Central Register of Controlled Trials were searched for all English language articles using specific MeSH terms (3dp OR 3D-print* OR three dimension* print*) AND (breast reconstruction). Only studies discussing the use of 3DP for surgical planning or as an educational tool in autologous breast reconstruction were included. Studies using 3DP as interventions or implants were excluded. Results: A total of 168 articles were identified, 13 of which were selected for inclusion. Risk of bias was low for 8 articles and moderate for 5 articles. Seven (53.8%) articles discussed 3DP usage in preoperative planning. Most papers (12, 92.3%) focused on 3DP models as guides for intraoperative identification of anatomical landmarks and 3DP molds as tools to achieve desired breast volume, shape, and projection. Only 4 (30.7%) articles discussed patient outcomes. Of the 4 (30.7%) articles that discussed education, only one of these properly assessed trainees and faculty using pre- and post-intervention surveys. Conclusion: The majority of 3DP research as an intraoperative guide and educational tool is concentrated outside of autologous breast reconstruction. Studies that do discuss this have found significantly higher success rates in dissecting true DIEP flaps when 3DP vascular modeling is used and can result in improved confidence and competence in surgical training for microsurgical anastomosis. Although 3DP has been shown to aid microsurgeons in preoperative planning, most research concentrates on the aid of this novel technology in dynamic, intraoperative decision making. Existing research has identified five 3DP breast molds, but studies have yet to compare these molds in a controlled setting to assess for superiority in feasibility and outcomes. There is little investigation into the usefulness of 3DP as an educational tool, and more research should be conducted as this methodology expands to cover more forms of autologous breast reconstruction.

Can artificial intelligence-driven cephalometric analysis replace manual tracing? A systematic review and meta-analysis.

This systematic review and meta-analysis aimed to investigate the accuracy and efficiency of artificial intelligence (AI)-driven automated landmark detection for cephalometric analysis on two-dimensional (2D) lateral cephalograms and three-dimensional (3D) cone-beam computed tomographic (CBCT) images. An electronic search was conducted in the following databases: PubMed, Web of Science, Embase, and grey literature with search timeline extending up to January 2024. Studies that employed AI for 2D or 3D cephalometric landmark detection were included. The selection of studies, data extraction, and quality assessment of the included studies were performed independently by two reviewers. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A meta-analysis was conducted to evaluate the accuracy of the 2D landmarks identification based on both mean radial error and standard error. Following the removal of duplicates, title and abstract screening, and full-text reading, 34 publications were selected. Amongst these, 27 studies evaluated the accuracy of AI-driven automated landmarking on 2D lateral cephalograms, while 7 studies involved 3D-CBCT images. A meta-analysis, based on the success detection rate of landmark placement on 2D images, revealed that the error was below the clinically acceptable threshold of 2mm (1.39mm; 95% confidence interval: 0.85-1.92mm). For 3D images, meta-analysis could not be conducted due to significant heterogeneity amongst the study designs. However, qualitative synthesis indicated that the mean error of landmark detection on 3D images ranged from 1.0 to 5.8mm. Both automated 2D and 3D landmarking proved to be time-efficient, taking less than 1min. Most studies exhibited a high risk of bias in data selection (n = 27) and reference standard (n = 29). The performance of AI-driven cephalometric landmark detection on both 2D cephalograms and 3D-CBCT images showed potential in terms of accuracy and time efficiency. However, the generalizability and robustness of these AI systems could benefit from further improvement. PROSPERO: CRD42022328800.

Ultrasound-Guided Off-Plan Lumbar Seated Erector Spinae Plane Blocks : Are There Advantages?

The erector spinae plane block (ESPB) has been widely used as a treatment strategy for a variety of acute and chronic painful conditions. ESPBs are typically performed under ultrasound guidance [USG] in an in-plane long axis approach, targeting the tip of the lumbar transverse process while the patient lies prone. Off-Plane Seated Injection Technique-ESPB [OPSIT-E] represents a useful alternative in situations where a standard prone spine injection would be technically challenged by circumstances which may include morbid obesity, orthopnoea, recent upper limbs surgeries, chest pain from recent pacemaker implant, and in a subjects were in-plane approaches may be complicated by skin lesions. A seated forward flexed off-plane injection position, may also flatten the lumbar lordosis, shift adipose tissue more anteriorly, lessening skin to target distance and facilitating bony landmark identification, in high BMI and hyperlordotic subjects. The relatively larger curved transverse arc radius of the curvilinear probe [GE G1-5] in comparison to its transverse arc, also appears to offer improved central field of skin-transducer contact, earlier needle visualization, improved acute angle trajectory visualization of deep structures, which may be due to less crepuscular beam dispersion in comparison to transverse probe orientation. Even with a linear probe, the orthogonal technique facilitates a more perpendicular vector, lessening needle transit to target distance, which may in turn decrease procedure time, and improve patient comfort. The OPSITE, may also be easier to teach, learn, and master, as other studies have generally reported a higher rate of off-lane injection success among novice vascular interventionists.

The cytoarchitectonic landscape revealed by deep learning method facilitated precise positioning in mouse neocortex.

Neocortex is a complex structure with different cortical sublayers and regions. However, the precise positioning of cortical regions can be challenging due to the absence of distinct landmarks without special preparation. To address this challenge, we developed a cytoarchitectonic landmark identification pipeline. The fluorescence micro-optical sectioning tomography method was employed to image the whole mouse brain stained by general fluorescent nucleotide dye. A fast 3D convolution network was subsequently utilized to segment neuronal somas in entire neocortex. By approach, the cortical cytoarchitectonic profile and the neuronal morphology were analyzed in 3D, eliminating the influence of section angle. And the distribution maps were generated that visualized the number of neurons across diverse morphological types, revealing the cytoarchitectonic landscape which characterizes the landmarks of cortical regions, especially the typical signal pattern of barrel cortex. Furthermore, the cortical regions of various ages were aligned using the generated cytoarchitectonic landmarks suggesting the structural changes of barrel cortex during the aging process. Moreover, we observed the spatiotemporally gradient distributions of spindly neurons, concentrated in the deep layer of primary visual area, with their proportion decreased over time. These findings could improve structural understanding of neocortex, paving the way for further exploration with this method.

Digital versus Manual Tracing in Cephalometric Analysis: A Systematic Review and Meta-Analysis.

Background: Over the years, various researchers have attempted to compare digital cephalometry with the conventional manual approach. There is a need to comprehensively analyze the findings from the earlier studies and determine the potential advantages and limitations of each method. The present systematic review aimed to compare the accuracy of digital and manual tracing in cephalometric analysis for the identification of skeletal and dental landmarks. Methods: A systematic search was performed using the keywords "Digital" AND "Manual" AND "Cephalometry" to identify relevant studies published in the English language in the past decade. The electronic data resources consulted for the elaborate search included the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, CINAHL, EMBASE, PsycINFO, Scopus, ERIC, and ScienceDirect with controlled vocabulary and free text terms. Results: A total of n = 20 studies were identified that fulfilled the inclusion and exclusion criteria within the timeframe of 2013 to 2023. The data extracted from the included articles and corresponding meta-analyses are presented in the text. Conclusions: The findings of the present systematic review and meta-analysis revealed trends suggesting that digital tracing may offer reliable measurements for specific cephalometric parameters efficiently and accurately. Orthodontists must consider the potential benefits of digital cephalometry, including time-saving and user-friendliness.