Set Of Landmarks Research Articles

Survival Analysis of Conversion Surgery in Borderline Resectable and Locally Advanced Unresectable Pancreatic Ductal Adenocarcinoma Addressing Selection and Immortal Time Bias: A Retrospective Single-Center Study.

The purpose of this study was to provide a detailed evaluation of the oncological advantages of surgery following neoadjuvant chemotherapy (NAC) for patients with borderline resectable (BR) or unresectable (UR) pancreatic ductal adenocarcinoma (PDAC), with a focus on minimizing biases. Recently, NAC has become the standard care for BR or UR locally advanced (UR-LA) PDAC, however, many studies have assessed survival benefits and favorable variables without consideration for biases, particularly immortal time bias. This study included patients diagnosed with BR or UR-LA PDAC at Juntendo University Hospital from 2019 to 2022. To mitigate bias, we applied methods such as propensity score matching (PSM), time-dependent covariate Cox proportional hazard regression analysis (TDC), landmark analysis, and multivariable Cox proportional hazards regression model. The study analyzed 124 patients, dividing them into a surgery group (n = 57) and a chemotherapy-only group (n = 67). After PSM, there were 21 matched pairs. Survival analysis using TDC analysis showed that the surgery group had significantly better overall survival compared with the chemotherapy-only group in both the entire cohort and the matched pairs. Cox regression analysis of the entire cohort also revealed a similar superiority of surgery, while the landmark analysis showed varying results depending on the landmark setting. After careful adjustment for selection and immortal time biases, surgery following NAC appears to significantly extend survival in patients with BR or UR PDAC.

Impact of scapula anatomical landmark positioning on scapular orientation using CT-based 3D models: an intra-observer repeatability and inter-observer reproducibility study

HypothesisThe primary objective of this study was to assess intra-observer repeatability and inter-observer reproducibility of the 3D coordinates of a set of scapula anatomical landmarks obtained by manual positioning on conventional supine CT-scan based scapula surface models. The secondary objective was to assess intra-observer repeatability and inter-observer reproducibility of the resulting 3D scapular orientation. It was hypothesized that the 3D scapular orientation reliability would be better or similar than established reliability of intra-operative baseplate positioning (i.e. version and inclination) in reverse total shoulder arthroplasty (rTSA). MethodsThree anatomical landmarks, i.e. acromial angle (AA), inferior angle (IA), and trigonum scapulae (TS), were manually positioned on 81 scapula surface models using updated landmark definitions. These models were obtained by the segmentation of CT-scan images acquired from patients undergoing elective rTSA procedures at the Department of Surgery at Geneva University Hospitals between May 2022 and December 2023. This procedure was repeated 3 times per scapula by 3 independent observers. A set of parameters corresponding to the 3D landmark coordinates (expressed in an average scapula coordinate system) and the 3D scapular orientation (expressed as three angles related to the retraction/protraction, lateral/medial rotation, and internal/external rotation) were computed. Intra-observer repeatability and inter-observer reproducibility were assessed for each of these parameters using an intra-class correlation as a relative reliability metric. Standard error of measurement (SEM) was also reported as an absolute reliability metric. ResultsIntra-observer ICC and inter-observer ICC ranged from poor to moderate for AA, poor to excellent for IA, and poor to excellent for TS. However, low SEM, ranging from 0.4 mm to 2.4 mm for intra-observer repeatability and from 0.2 mm to 1.8 mm for inter-observer reproducibility, were obtained for every coordinate of each anatomical landmark. This results in poor to moderate intra-observer ICC and inter-observer ICC for scapula angular orientation. Again, low SEM, ranging from 0.4° to 0.8° for intra-observer repeatability and 0.4° to 0.6° for inter-observer reproducibility, were obtained, arguing for reliable measurements. Discussion and ConclusionThis study demonstrates that manual positioning of the three scapula anatomical landmarks recommended by the International Society of Biomechanics can be performed in a reliable manner across measures and observers on surface bone models obtained from CT-scan images. However, a clear and standardized definition of these landmarks is needed to ensure consistency across measurements.

Online landmark replacement for out-of-sample dimensionality reduction methods

A strategy to assist visualization and analysis of large and complex datasets is dimensionality reduction, with which one maps each data point into a low-dimensional manifold. However, various dimensionality reduction techniques are computationally infeasible for large data. Out-of-sample techniques aim to resolve this difficulty; they only apply the dimensionality reduction technique on a small portion of data, referred to as landmarks, and determine the embedding coordinates of the other points using landmarks as references. Out-of-sample techniques have been applied to online settings, or when data arrive as time series. However, existing online out-of-sample techniques use either all the previous data points as landmarks or the fixed set of landmarks and therefore are potentially not good at capturing the geometry of the entire dataset when the time series is non-stationary. To address this problem, we propose an online landmark replacement algorithm for out-of-sample techniques using geometric graphs and the minimal dominating set on them. We mathematically analyse some properties of the proposed algorithm, particularly focusing on the case of landmark multi-dimensional scaling as the out-of-sample technique, and test its performance on synthetic and empirical time-series data.

First insight into the genomes of the Pulmonaria officinalis group (Boraginaceae) provided by repeatome analysis and comparative karyotyping

BackgroundThe genus Pulmonaria (Boraginaceae) represents a taxonomically complex group of species in which morphological similarity contrasts with striking karyological variation. The presence of different numbers of chromosomes in the diploid state suggests multiple hybridization/polyploidization events followed by chromosome rearrangements (dysploidy). Unfortunately, the phylogenetic relationships and evolution of the genome, have not yet been elucidated. Our study focused on the P. officinalis group, the most widespread species complex, which includes two morphologically similar species that differ in chromosome number, i.e. P. obscura (2n = 14) and P. officinalis (2n = 16). Ornamental cultivars, morphologically similar to P. officinalis (garden escapes), whose origin is unclear, were also studied. Here, we present a pilot study on genome size and repeatome dynamics of these closely related species in order to gain new information on their genome and chromosome structure.ResultsFlow cytometry confirmed a significant difference in genome size between P. obscura and P. officinalis, corresponding to the number of chromosomes. Genome-wide repeatome analysis performed on genome skimming data showed that retrotransposons were the most abundant repeat type, with a higher proportion of Ty3/Gypsy elements, mainly represented by the Tekay lineage. Comparative analysis revealed no species-specific retrotransposons or striking differences in their copy number between the species. A new set of chromosome-specific cytogenetic markers, represented by satellite DNAs, showed that the chromosome structure in P. officinalis was more variable compared to that of P. obscura. Comparative karyotyping supported the hybrid origin of putative hybrids with 2n = 15 collected from a mixed population of both species and outlined the origin of ornamental garden escapes, presumably derived from the P. officinalis complex.ConclusionsLarge-scale genome size analysis and repeatome characterization of the two morphologically similar species of the P. officinalis group improved our knowledge of the genome dynamics and differences in the karyotype structure. A new set of chromosome-specific cytogenetic landmarks was identified and used to reveal the origin of putative hybrids and ornamental cultivars morphologically similar to P. officinalis.

Registration accuracy comparing different rendering techniques on local vs external virtual 3D liver model reconstruction for vascular landmark setting by intraoperative ultrasound in augmented reality navigated liver resection

PurposeAugmented reality navigation in liver surgery still faces technical challenges like insufficient registration accuracy. This study compared registration accuracy between local and external virtual 3D liver models (vir3DLivers) generated with different rendering techniques and the use of the left vs right main portal vein branch (LPV vs RPV) for landmark setting. The study should further examine how registration accuracy behaves with increasing distance from the ROI.MethodsRetrospective registration accuracy analysis of an optical intraoperative 3D navigation system, used in 13 liver tumor patients undergoing liver resection/thermal ablation.Results109 measurements in 13 patients were performed. Registration accuracy with local and external vir3DLivers was comparable (8.76 ± 0.9 mm vs 7.85 ± 0.9 mm; 95% CI = -0.73 to 2.55 mm; p = 0.272). Registrations via the LPV demonstrated significantly higher accuracy than via the RPV (6.2 ± 0.85 mm vs 10.41 ± 0.99 mm, 95% CI = 2.39 to 6.03 mm, p < 0.001). There was a statistically significant positive but weak correlation between the accuracy (dFeature) and the distance from the ROI (dROI) (r = 0.298; p = 0.002).ConclusionDespite basing on different rendering techniques both local and external vir3DLivers have comparable registration accuracy, while LPV-based registrations significantly outperform RPV-based ones in accuracy. Higher accuracy can be assumed within distances of up to a few centimeters around the ROI.

A machine learning approach for age prediction based on trigeminal landmarks

ObjectiveThe aim of this study was to estimate the chronological age (CA) of a growing individual using a new machine learning approach on Cone Beam Computed Tomography (CBCT). Materials and methodsThe dataset included 48 CBCT and hand-wrist radiographs of growing individuals. 12 landmarks related to trigeminal trajectories were plotted on each CBCT and principal component analysis was applied for dimensionality reduction. The estimated CA was obtained using a decision tree. Finally, a genetic algorithm was implemented to select the best set of landmarks that would optimize the estimation. The age was also assessed following Greulich and Pyle's (GP) method on hand-wrist radiographs. The results (GP and Machine Learning) were then compared to the true CA. ResultsAmong the 12 landmarks, the genetic algorithm selected 7 optimal features, and 12 principal components out of 36. The best results for age prediction were obtained by a combination of genetic algorithm, principal component analysis, and regression tree where the Mean Squared Error (MSE) and Mean Absolute Error (MAE) were respectively 1.29 and 0.92. These outcomes showed improved accuracy compared to those of the hand-wrist method (MSE = 2.038 and MAE = 1.775). ConclusionsA numerical application on a dataset of CBCT showed that the proposed machine learning method achieved an improved accuracy compared to conventional methods and had satisfying performance in assessing age for forensic purposes. Validation of the presented method on a larger and more diverse sample would pave the way for future applications in forensic science as a tool for age prediction.

Fast connectivity gradient approximation: maintaining spatially fine-grained connectivity gradients while reducing computational costs

Brain connectome analysis suffers from the high dimensionality of connectivity data, often forcing a reduced representation of the brain at a lower spatial resolution or parcellation. This is particularly true for graph-based representations, which are increasingly used to characterize connectivity gradients, capturing patterns of systematic spatial variation in the functional connectivity structure. However, maintaining a high spatial resolution is crucial for enabling fine-grained topographical analysis and preserving subtle individual differences that might otherwise be lost. Here we introduce a computationally efficient approach to establish spatially fine-grained connectivity gradients. At its core, it leverages a set of landmarks to approximate the underlying connectivity structure at the full spatial resolution without requiring a full-scale vertex-by-vertex connectivity matrix. We show that this approach reduces computational time and memory usage while preserving informative individual features and demonstrate its application in improving brain-behavior predictions. Overall, its efficiency can remove computational barriers and enable the widespread application of connectivity gradients to capture spatial signatures of the connectome. Importantly, maintaining a spatially fine-grained resolution facilitates to characterize the spatial transitions inherent in the core concept of gradients of brain organization.

Strategies for Minimizing the Effects of Observer Variability on Sagittal Parameter Measurements of the Spine.

Exploratory-descriptive study. Evaluate the influence of observer variability on the measurements of both thoracic kyphosis (TK) and lumbar lordosis (LL) obtained with anatomic and functional spinal segmentation methods. Parametric analysis for spinal surgery planning typically relies on anatomic parameters. However, incorporating functional parameters that consider the vertebrae orientation is important to minimizing surgical calculation errors. The authors developed parametric analysis software that integrates traditional and functional methodologies. The proposed method included functional thoracic kyphosis and functional lumbar lordosis calculated from the lines normal to the inflection points of the spine model. Using a synthetic lateral X-ray, the observer variability was computer-simulated generating 20 landmark sets that replicate the annotations of 20 observers. The analysis also included 10 clinical X-rays, annotated twice by 3 judges with a minimum 1-week interval. The spinal curvature angles were derived using the anatomic and functional methods. Statistical analysis were performed for comparison. For the synthetic X-ray, the proposed method presented significantly less variability: TK (<±2.5 degrees, P=0.00023) and LL (<±5 degrees, P=0.00012). For the clinical X-rays, the interobserver reliability analysis yielded higher intraclass correlation coefficients (ICC) for functional TK (ICC>0.97) and functional LL (ICC>0.87) than for TK (ICC<0.91) and LL (ICC<0.89). Statistically significant differences were observed for both TK (P=0.001) and LL (P=0.030). Under the traditional method, observer variability led to measurement differences surpassing ±19 degrees, whereas differences with the proposed method were within ±10 degrees for both parameters. The vertebral endplate is not the most suitable place to measure spinal sagittal curvatures. Small changes in landmark position significantly alter the measured Cobb angle. The proposed method offers a substantial advantage regarding the influence of observer variability, in addition to the more individualized analysis.

Leveraging Descriptor Learning and Functional Map-based Shape Matching for Automatic Landmark Acquisition.

Geometric morphometrics is widely employed across the biological sciences for the quantification of morphological traits. However, the scalability of these methods to large datasets is hampered by the requisite placement of landmarks, which can be laborious and time consuming if done manually. Additionally, the selected landmarks embody a particular hypothesis regarding the critical geometry pertinent to the biological inquiry at hand. Modifying this hypothesis lacks flexibility, necessitating the acquisition of an entirely new set of landmarks on the entire dataset to reflect any theoretical adjustments. In our research, we investigate the precision and accuracy of landmarks derived from the comprehensive set of functional correspondences acquired through the functional map framework of geometry processing. We use a deep functional map network to learn shape descriptors that effectively yield functional map-based and point-to-point correspondences between the specimens in our dataset. We then interrogate these maps to identify corresponding landmarks given manually placed landmarks from the entire dataset. We assess our method by automating the landmarking process on a dataset comprising mandibles from various rodent species, comparing its efficacy against MALPACA, a cutting-edge technique for automatic landmark placement. Compared to MALPACA, our model is notably faster and maintains competitive accuracy. The Root Mean Square Error (RMSE) analysis reveals that while MALPACA generally exhibits the lowest RMSE, our models perform comparably, especially with smaller training datasets, suggesting strong generalizability. Visual evaluations confirm the precision of our landmark placements, with deviations remaining within an acceptable range. These findings underscore the potential of unsupervised learning models in anatomical landmark placement, providing a viable and efficient alternative to traditional methods.

A novel approach to craniofacial analysis using automated 3D landmarking of the skull

Automatic dense 3D surface registration is a powerful technique for comprehensive 3D shape analysis that has found a successful application in human craniofacial morphology research, particularly within the mandibular and cranial vault regions. However, a notable gap exists when exploring the frontal aspect of the human skull, largely due to the intricate and unique nature of its cranial anatomy. To better examine this region, this study introduces a simplified single-surface craniofacial bone mask comprising of 6707 quasi-landmarks, which can aid in the classification and quantification of variation over human facial bone surfaces. Automatic craniofacial bone phenotyping was conducted on a dataset of 31 skull scans obtained through cone-beam computed tomography (CBCT) imaging. The MeshMonk framework facilitated the non-rigid alignment of the constructed craniofacial bone mask with each individual target mesh. To gauge the accuracy and reliability of this automated process, 20 anatomical facial landmarks were manually placed three times by three independent observers on the same set of images. Intra- and inter-observer error assessments were performed using root mean square (RMS) distances, revealing consistently low scores. Subsequently, the corresponding automatic landmarks were computed and juxtaposed with the manually placed landmarks. The average Euclidean distance between these two landmark sets was 1.5 mm, while centroid sizes exhibited noteworthy similarity. Intraclass coefficients (ICC) demonstrated a high level of concordance (> 0.988), with automatic landmarking showing significantly lower errors and variation. These results underscore the utility of this newly developed single-surface craniofacial bone mask, in conjunction with the MeshMonk framework, as a highly accurate and reliable method for automated phenotyping of the facial region of human skulls from CBCT and CT imagery. This craniofacial template bone mask expansion of the MeshMonk toolbox not only enhances our capacity to study craniofacial bone variation but also holds significant potential for shedding light on the genetic, developmental, and evolutionary underpinnings of the overall human craniofacial structure.

A robot-based surveillance system for recognising distress hand signal

Abstract Unfortunately, there are still cases of domestic violence or situations where it is necessary to call for help without arousing the suspicion of the aggressor. In these situations, the help signal devised by the Canadian Women’s Foundation has proven to be effective in reporting a risky situation. By displaying a sequence of hand signals, it is possible to report that help is needed. This work presents a vision-based system that detects this sequence and implements it in a social robot, so that it can automatically identify unwanted situations and alert the authorities. The gesture recognition pipeline presented in this work is integrated into a cognitive architecture used to generate behaviours in robots. In this way, the robot interacts with humans and is able to detect if a person is calling for help. In that case, the robot will act accordingly without alerting the aggressor. The proposed vision system uses the MediaPipe library to detect people in an image and locate the hands, from which it extracts a set of hand landmarks that identify which gesture is being made. By analysing the sequence of detected gestures, it can identify whether a person is performing the distress hand signal with an accuracy of 96.43%.

Generating Multiple 4D Expression Transitions by Learning Face Landmark Trajectories

In this work, we address the problem of 4D facial expressions generation. This is usually addressed by animating a neutral 3D face to reach an expression peak, and then get back to the neutral state. In the real world though, people show more complex expressions, and switch from one expression to another. We thus propose a new model that generates transitions between different expressions, and synthesizes long and composed 4D expressions. This involves three sub-problems: (i) modeling the temporal dynamics of expressions, (ii) learning transitions between them, and (iii) deforming a generic mesh. We propose to encode the temporal evolution of expressions using the motion of a set of 3D landmarks, that we learn to generate by training a manifold-valued GAN (Motion3DGAN). To allow the generation of composed expressions, this model accepts two labels encoding the starting and the ending expressions. The final sequence of meshes is generated by a Sparse2Dense mesh Decoder (S2D-Dec) that maps the landmark displacements to a dense, per-vertex displacement of a known mesh topology. By explicitly working with motion trajectories, the model is totally independent from the identity. Extensive experiments on five public datasets show that our proposed approach brings significant improvements with respect to previous solutions, while retaining good generalization to unseen data.

Exercise quantification from single camera view markerless 3D pose estimation

Sports physiotherapists and coaches are tasked with evaluating the movement quality of athletes across the spectrum of ability and experience. However, the accuracy of visual observation is low and existing technology outside of expensive lab-based solutions has limited adoption, leading to an unmet need for an efficient and accurate means to measure static and dynamic joint angles during movement, converted to movement metrics useable by practitioners. This paper proposes a set of pose landmarks for computing frequently used joint angles as metrics of interest to sports physiotherapists and coaches in assessing common strength-building human exercise movements. It then proposes a set of rules for computing these metrics for a range of common exercises (single and double drop jumps and counter-movement jumps, deadlifts and various squats) from anatomical key-points detected using video, and evaluates the accuracy of these using a published 3D human pose model trained with ground truth data derived from VICON motion capture of common rehabilitation exercises. Results show a set of mathematically defined metrics which are derived from the chosen pose landmarks, and which are sufficient to compute the metrics for each of the exercises under consideration. Comparison to ground truth data showed that root mean square angle errors were within 10° for all exercises for the following metrics: shin angle, knee varus/valgus and left/right flexion, hip flexion and pelvic tilt, trunk angle, spinal flexion lower/upper/mid and rib flare. Larger errors (though still all within 15°) were observed for shoulder flexion and ASIS asymmetry in some exercises, notably front squats and drop-jumps. In conclusion, the contribution of this paper is that a set of sufficient key-points and associated metrics for exercise assessment from 3D human pose have been uniquely defined. Further, we found generally very good accuracy of the Strided Transformer 3D pose model in predicting these metrics for the chosen set of exercises from a single mobile device camera, when trained on a suitable set of functional exercises recorded using a VICON motion capture system. Future assessment of generalization is needed.

Deep reinforcement learning and convolutional autoencoders for anomaly detection of congenital inner ear malformations in clinical CT images

Detection of abnormalities within the inner ear is a challenging task even for experienced clinicians. In this study, we propose an automated method for automatic abnormality detection to provide support for the diagnosis and clinical management of various otological disorders. We propose a framework for inner ear abnormality detection based on deep reinforcement learning for landmark detection which is trained uniquely in normative data. In our approach, we derive two abnormality measurements: Dimage and Uimage. The first measurement, Dimage, is based on the variability of the predicted configuration of a well-defined set of landmarks in a subspace formed by the point distribution model of the location of those landmarks in normative data. We create this subspace using Procrustes shape alignment and Principal Component Analysis projection. The second measurement, Uimage, represents the degree of hesitation of the agents when approaching the final location of the landmarks and is based on the distribution of the predicted Q-values of the model for the last ten states. Finally, we unify these measurements in a combined anomaly measurement called Cimage. We compare our method’s performance with a 3D convolutional autoencoder technique for abnormality detection using the patch-based mean squared error between the original and the generated image as a basis for classifying abnormal versus normal anatomies. We compare both approaches and show that our method, based on deep reinforcement learning, shows better detection performance for abnormal anatomies on both an artificial and a real clinical CT dataset of various inner ear malformations with an increase of 11.2% of the area under the ROC curve. Our method also shows more robustness against the heterogeneous quality of the images in our dataset.

Inequality-Constrained 3D Morphable Face Model Fitting.

3D morphable model (3DMM) fitting on 2D data is traditionally done via unconstrained optimization with regularization terms to ensure that the result is a plausible face shape and is consistent with a set of 2D landmarks. This paper presents inequality-constrained 3DMM fitting as the first alternative to regularization in optimization-based 3DMM fitting. Inequality constraints on the 3DMM's shape coefficients ensure face-like shapes without modifying the objective function for smoothness, thus allowing for more flexibility to capture person-specific shape details. Moreover, inequality constraints on landmarks increase robustness in a way that does not require per-image tuning. We show that the proposed method stands out with its ability to estimate person-specific face shapes by jointly fitting a 3DMM to multiple frames of a person. Further, when used with a robust objective function, namely gradient correlation, the method can work "in-the-wild" even with a 3DMM constructed from controlled data. Lastly, we show how to use the log-barrier method to efficiently implement the method. To our knowledge, we present the first 3DMM fitting framework that requires no learning yet is accurate, robust, and efficient. The absence of learning enables a generic solution that allows flexibility in the input image size, interchangeable morphable models, and incorporation of camera matrix.

Inner ear morphology in wild versus laboratory house mice.

The semicircular canals of the inner ear are involved in balance and velocity control. Being crucial to ensure efficient mobility, their morphology exhibits an evolutionary conservatism attributed to stabilizing selection. Release of selection in slow-moving animals has been argued to lead to morphological divergence and increased inter-individual variation. In its natural habitat, the house mouse Mus musculus moves in a tridimensional space where efficient balance is required. In contrast, laboratory mice in standard cages are severely restricted in their ability to move, which possibly reduces selection on the inner ear morphology. This effect was tested by comparing four groups of mice: several populations of wild mice trapped in commensal habitats in France; their second-generation laboratory offspring, to assess plastic effects related to breeding conditions; a standard laboratory strain (Swiss) that evolved for many generations in a regime of mobility reduction; and hybrids between wild offspring and Swiss mice. The morphology of the semicircular canals was quantified using a set of 3D landmarks and semi-landmarks analyzed using geometric morphometric protocols. Levels of inter-population, inter-individual (disparity) and intra-individual (asymmetry) variation were compared. All wild mice shared a similar inner ear morphology, in contrast to the important divergence of the Swiss strain. The release of selection in the laboratory strain obviously allowed for an important and rapid drift in the otherwise conserved structure. Shared traits between the inner ear of the lab strain and domestic pigs suggested a common response to mobility reduction in captivity. The lab-bred offspring of wild mice also differed from their wild relatives, suggesting plastic response related to maternal locomotory behavior, since inner ear morphology matures before birth in mammals. The signature observed in lab-bred wild mice and the lab strain was however not congruent, suggesting that plasticity did not participate to the divergence of the laboratory strain. However, contrary to the expectation, wild mice displayed slightly higher levels of inter-individual variation than laboratory mice, possibly due to the higher levels of genetic variance within and among wild populations compared to the lab strain. Differences in fluctuating asymmetry levels were detected, with the laboratory strain occasionally displaying higher asymmetry scores than its wild relatives. This suggests that there may indeed be a release of selection and/or a decrease in developmental stability in the laboratory strain.

Suggestions for Introducing Some New Terms in Pelvic and Acetabular Surgery

Background. The rapid advancement of modern surgical methods for treating pelvic bone fractures has underscored the necessity for developing a new terminological framework. This is because the classical anatomical terminology of the pelvis no longer aligns with the demands of the therapeutic process and scientific research in this field. The traditional set of anatomical names and landmarks falls short in providing detailed descriptions of all intricacies of injuries when employing contemporary surgical techniques. The existing terminology system needs to catch up with the level of contemporary pelvic surgery, enabling a comprehensive and understandable characterization of existing pathology and the treatment being administered for all medical professionals.&#x0D; Purpose of the study was to create names for certain parts of the pelvic bones and their areas that currently lack specific designations and to propose the developed terms for professional discussion.&#x0D; Methods. A retrospective analysis was conducted on X-rays and computer tomography scans of patients with pelvic bone injuries, performed from 2020 to 2022. A list of potential new anatomical terms was compiled through a literature review.&#x0D; Results. In several cases, we encountered a deficiency of terms in diagnosing pelvic injuries and describing surgical procedures. New terms were developed to denote areas of the pelvis and their injuries, including the pubic bone base, vertical fractures of the pubic bone base, longitudinal fractures of the pubic bone base, incomplete rupture of the pubic symphysis, the base of the ilium, longitudinal fracture of the iliac base, fracture-subluxation and fracture-dislocation of the iliac base, calcar of the iliac bone, calcar spike, and the bone corridor.&#x0D; Conclusions. The incorporation of new anatomical terms into clinical practice will help enhance the precision of diagnosis and surgical planning in pelvic fractures. Standardizing the terminology will promote uniformity in approaches and knowledge sharing among specialists, ultimately improving the quality of surgical care for patients with pelvic injuries.

In-hospital and 1-year outcomes of patients without modifiable risk factors presenting with acute coronary syndrome undergoing PCI: a sex-stratified analysis

Abstract Background The standard modifiable cardiovascular risk factors (SMuRFs: hypertension, diabetes mellitus, dyslipidemia, and cigarette smoking) are known to play a major role in the pathogenesis of coronary heart disease (CHD). Despite endeavors aiming at primary and secondary prevention of these risk factors, CHD has remained a leading cause of mortality and morbidity worldwide. A considerable proportion of patients admitted with acute coronary syndrome (ACS) have no SMuRFs, the outcomes of whom following percutaneous coronary intervention (PCI) are debated. Further, sex differences within this population have yet to be established. Purpose The present study aimed to evaluate the association between SMuRF-less status and post-PCI outcomes among patients with ACS. Further, we sought to determine possible sex differences in the post-PCI outcomes related to the SMuRF-less status. Methods This retrospective cohort study included 7847 patients with ACS who underwent PCI. The study outcomes were all-cause mortality and major adverse cardio-cerebrovascular events (MACCE). The association between the absence of SMuRFs (SMuRF-less status) and outcomes among all the patients and each sex was assessed using logistic and Cox proportional hazard regressions. A separate landmark analysis with the landmark (cutoff) set at 30 days after the index procedure was performed as well. Models were adjusted for age, sex, body mass index, chronic obstructive pulmonary disease, cerebrovascular accident, previous myocardial infarction, estimated glomerular filtration rate, ejection fraction, and left main disease. Results Approximately 11% of the study population had none of the SMuRFs. The SMuRF-less and SMuRF+ groups aged 63.10 ±8.91 (85.1% males) and 62.53 ± 10.91 years (74.9% males), respectively. During 12.13 [11.99-12.36] months of follow-up, the followings were respective event numbers in the SMuRF-less and SMuRF+ groups: all-cause mortality: 35 (4.1%) and 273 (3.9%); MACCE: 99 (11.6%) and 898 (12.8%). All-cause mortality (adjusted-hazard ratio (HR): 1.01, 95%CI: 0.88-1.46, P: 0.731), and MACCE (adjusted-HR: 0.93, 95%CI:0.81-1.12, P: 0.412) did not differ between patients with and without SMuRFs (Figure 1). Sex-stratified analyses recapitulated similar outcomes between SMuRF+ and SMuRF-less men. In contrast, SMuRF-less women had significantly higher all-cause mortality (adjusted-HR:1.41, 95%CI: 1.02-3.21, P: 0.008) than SMuRF+ women (Figure 1). Notably, following landmark analyses, this significant association among women disappeared (adjusted-HR: 0.99, 95%CI: 0.41-2.59, P:0.991), implying that its significance stemmed, to some extent, from higher 30-day mortality (Figure 1,2). Conclusions Almost one in 10 patients with ACS who underwent PCI had no SMuRFs. The absence of SMuRFs did not confer any benefit in terms of in-hospital mortality, one-year mortality, and MACCE. Even worse, SMuRF-less women paradoxically had an excessive risk of one-year mortality.

Approaches to studying endocranial morphology in Euarchontoglires: Assessing sources of error for a novel and biologically informative set of landmarks

Approaches to studying endocranial morphology in Euarchontoglires: Assessing sources of error for a novel and biologically informative set of landmarks

Applying artificial intelligence to determination of legal age of majority from radiographic data

Forensic odontologists use biological patterns to estimate chronological age for the judicial system. The age of majority is a legally significant period with a limited set of reliable oral landmarks. Currently, experts rely on the questionable development of third molars to assess whether litigants can be prosecuted as legal adults. Identification of new and novel patterns may illuminate features more dependably indicative of chronological age, which have, until now, remained unseen. Unfortunately, biased perceptions and limited cognitive capacity compromise the ability of researchers to notice new patterns. The present study demonstrates how artificial intelligence can break through identification barriers and generate new estimation modalities. A convolutional neural network was trained with 4003 panoramic-radiographs to sort subjects into 'under-18' and 'over-18' age categories. The resultant architecture identified legal adults with a high predictive accuracy equally balanced between precision, specificity and recall. Moving forward, AI-based methods could improve courtroom efficiency, stand as automated assessment methods and contribute to our understanding of biological ageing.