Analysis Of Imaging Data Research Articles

ROME/REA: Three-year, Tri-color Timeseries Photometry of the Galactic Bulge

The Robotic Observations of Microlensing Events/Reactive Event Assessment Survey was a Key Project at Las Cumbres Observatory (hereafter LCO) which continuously monitored 20 selected fields (3.76 sq.deg) in the Galactic Bulge throughout their seasonal visibility window over a three-year period, between 2017 March and 2020 March. Observations were made in three optical passbands (SDSS −g′ , −r′ , −i′ ), and LCO’s multi-site telescope network enabled the survey to achieve a typical cadence of ∼10 hr in i′ and ∼15 hr in g′ and r′ . In addition, intervals of higher cadence (<1 hr) data were obtained during monitoring of key microlensing events within the fields. This paper describes the Difference Image Analysis data reduction pipeline developed to process these data, and the process for combining the photometry from LCO’s three observing sites in the Southern Hemisphere. The full timeseries photometry for all ∼8 million stars, down to a limiting magnitude of i ∼ 18 mag is provided in the data release accompanying this paper, and samples of the data are presented for exemplar microlensing events, illustrating how the tri-band data are used to derive constraints on the microlensing source star parameters, a necessary step in determining the physical properties of the lensing object. The timeseries data also enables a wealth of additional science, for example in characterizing long-timescale stellar variability, and a few examples of the data for known variables are presented.

Molding Process Retaining Gold Nanoparticle Assembly Structures during Transfer to a Polycarbonate Surface.

The immobilization of gold nanoparticle (AuNP) linear surface assemblies on polycarbonate (PC) melt surface via molding is investigated. The order of the particle assemblies is preserved during the molding process. The assemblies on PC exhibit plasmonic coupling features and dichroic properties. The structure of the assemblies is quantified based on Scanning Electron Microscopy (SEM) and image analysis data using an orientational order parameter. The transfer process from mold to melt shows high structural fidelity. The order parameter of around 0.98 reflects the orientation of the lines and remains unaffected, independent of the injection direction of the melt relative to the particle lines. This is discussed in the frame of fountain flow during injection molding. The particles were permanently fixed and withstood the injection molding process, detachment of the substrate, and extraction in boiling ethanol. The plasmonic particles coupled strongly within the dense nanoparticle lines to produce anisotropic optical properties, as quantified by dichroic ratios of 0.28 and 0.52 using ultraviolet-visible-near-infrared (UV-Vis-NIR) spectroscopy. AuNP line assemblies on a polymer surface may be a basis for plasmonic devices like surface-enhanced Raman scattering (SERS) sensors or a precursor for nanowires. Their embedding via injection molding constitutes an important link between particle-self-assembly approaches for optically functional surfaces and polymer processing techniques.

Recent Applications of Cloud Computing in Medical Imaging: Advances in Medical Image Analysis and Storage of Medical Imaging Data

Recent Applications of Cloud Computing in Medical Imaging: Advances in Medical Image Analysis and Storage of Medical Imaging Data

Unveiling Novel Structural Biomarkers for the Diagnosis of Glaucoma.

Glaucoma, a leading cause of irreversible blindness, poses a significant global health burden. Early detection is crucial for effective management and prevention of vision loss. This study presents a collection of novel structural biomarkers in glaucoma diagnosis. By employing advanced imaging techniques and data analysis algorithms, we now can recognize indicators of glaucomatous progression. Many research studies have revealed a correlation between the structural changes in the eye or brain, particularly in the optic nerve head and retinal nerve fiber layer, and the progression of glaucoma. These biomarkers demonstrate value in distinguishing glaucomatous eyes from healthy ones, even in the early stages of the disease. By facilitating timely detection and monitoring, they hold the potential to mitigate vision impairment and improve patient outcomes. This study marks an advancement in the field of glaucoma, offering a promising avenue for enhancing the diagnosis and possible management.

Impact of functional electrical stimulation on nerve-damaged muscles by quantifying fat infiltration using deep learning.

Quantitative imaging in life sciences has evolved into a powerful approach combining advanced microscopy acquisition and automated analysis of image data. The focus of the present study is on the imaging-based evaluation of the posterior cricoarytenoid muscle (PCA) influenced by long-term functional electrical stimulation (FES), which may assist the inspiration of patients with bilateral vocal fold paresis. To this end, muscle cross-sections of the PCA of sheep were examined by quantitative image analysis. Previous investigations of the muscle fibers and the collagen amount have not revealed signs of atrophy and fibrosis due to FES by a laryngeal pacemaker. It was therefore hypothesized that regardless of the stimulation parameters the fat in the muscle cross-sections would not be significantly altered. We here extending our previous investigations using quantitative imaging of intramuscular fat in cross-sections. In order to perform this analysis both reliably and faster than a qualitative evaluation and time-consuming manual annotation, the selection of the automated method was of crucial importance. To this end, our recently established deep neural network IMFSegNet, which provides more accurate results compared to standard machine learning approaches, was applied to more than 300 H&E stained muscle cross-sections from 22 sheep. It was found that there were no significant differences in the amount of intramuscular fat between the PCA with and without long-term FES, nor were any significant differences found between the low and high duty cycle stimulated groups. This study on a human-like animal model not only confirms the hypothesis that FES with the selected parameters has no negative impact on the PCA, but also demonstrates that objective and automated deep learning-based quantitative imaging is a powerful tool for such a challenging analysis.

Novel systematic processing of cardiac magnetic resonance imaging identifies target regions associated with infarct-related ventricular tachycardia

Abstract Introduction In the clinic, complete characterization of reentrant ventricular tachycardia (VT) circuits using classical electrophysiological criteria and state-of-the-art high density mapping during VT is complex, and often cannot be completed because of hemodynamic instability. This common limitation has motivated the implementation of mapping strategies during sinus rhythm or ventricular pacing aiming to identify scar regions associated with VT isthmuses or potential arrhythmogenicity. Complementary imaging-based strategies including advanced analyses of scar tissue characteristics have been also proposed for procedure planning and identification of potential arrhythmogenic regions. However, the most recent expert consensus report on catheter ablation of ventricular arrhythmia highlights the lack of agreement on the preferable signal intensity (SI) cut-off values for assessment of tissue heterogeneity and scar areas on late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) images. The latter makes LGE-CMR imaging processing highly operator-dependent and potentially sensitive to selection bias. Purpose We aimed at developing and validating a systematic processing approach on LGE-CMR images to identify potential VT corridors that may include protected isthmuses associated with VT maintenance. Methods Translational study including 18 pigs with established myocardial infarction and inducible VT undergoing in vivo characterization of the anatomical and functional myocardial substrate associated with VT maintenance. Clinical validation was conducted in a multicenter series of 33 patients with ischemic cardiomyopathy undergoing VT ablation. Three-dimensional CMR-LGE images were processed using systematic scanning of 15 signal intensity cut-off ranges to obtain surface visualization of all potential VT corridors. Analysis and comparisons of imaging and electrophysiological data were performed in individuals with full electrophysiological characterization of the isthmus sites of at least one VT morphology. Results In both the experimental pig model and patients undergoing VT ablation, all the electrophysiologically-defined isthmus sites (n=11 and n=19, respectively) showed overlapping regions with CMR-based potential VT corridors. Such imaging-based VT corridors were less specific than electrophysiologically-guided ablation lesions at functionally relevant isthmus sites (Figure). However, in patients, decreasing from 15 to 7 the number of signal intensity cut-off ranges for systematic imaging processing significantly reduced the area of imaging-based potential VT corridors, without diminishing the capability to detect functionally relevant VT isthmus sites. Conclusions Systematic processing of 3D LGE-CMR images provides unbiased identification of imaging based potential VT corridors that contain functionally relevant protected isthmus sites.

Verinote - Fake Currency Detection Using Convolutional Neural Network

The proliferation of counterfeit money poses a significant threat to financial systems and economies worldwide. To solve this problem, advanced technological solutions have emerged, such as the counterfeit money detection system “FCDS”. The system leverages advanced image processing, machine learning and data analysis techniques to identify counterfeit bills accurately and effectively [1]. FCDS works by analyzing various security features found on legal tender, including watermarks, security chains, holograms and microprinting. Using image recognition and pattern analysis, the system distinguishes between real and fake money. Machine learning algorithms play a central role in training systems to recognize the subtle nuances that counterfeiters try to reproduce. FCDS can be deployed in a variety of contexts, from banks and financial institutions to retail businesses, providing a robust and scalable solution to combat counterfeiting [1]. By quickly identifying fraudulent notes, it helps prevent economic loss and maintain the integrity of financial transactions. This summary describes the nature of counterfeit currency detection systems and its importance in maintaining financial security and confidence. Its integration into modern banking and commerce systems represents an important step towards a counterfeit-proof financial ecosystem.

Integration of case-based learning and three-dimensional printing for tetralogy of fallot instruction in clinical medical undergraduates: a randomized controlled trial

BackgroundCase-based learning (CBL) methods have gained prominence in medical education, proving especially effective for preclinical training in undergraduate medical education. Tetralogy of Fallot (TOF) is a congenital heart disease characterized by four malformations, presenting a challenge in medical education due to the complexity of its anatomical pathology. Three-dimensional printing (3DP), generating physical replicas from data, offers a valuable tool for illustrating intricate anatomical structures and spatial relationships in the classroom. This study explores the integration of 3DP with CBL teaching for clinical medical undergraduates.MethodsSixty senior clinical medical undergraduates were randomly assigned to the CBL group and the CBL-3DP group. Computed tomography imaging data from a typical TOF case were exported, processed, and utilized to create four TOF models with a color 3D printer. The CBL group employed CBL teaching methods, while the CBL-3DP group combined CBL with 3D-printed models. Post-class exams and questionnaires assessed the teaching effectiveness of both groups.ResultsThe CBL-3DP group exhibited improved performance in post-class examinations, particularly in pathological anatomy and TOF imaging data analysis (P < 0.05). Questionnaire responses from the CBL-3DP group indicated enhanced satisfaction with teaching mode, promotion of diagnostic skills, bolstering of self-assurance in managing TOF cases, and cultivation of critical thinking and clinical reasoning abilities (P < 0.05). These findings underscore the potential of 3D printed models to augment the effectiveness of CBL, aiding students in mastering instructional content and bolstering their interest and self-confidence in learning.ConclusionThe fusion of CBL with 3D printing models is feasible and effective in TOF instruction to clinical medical undergraduates, and worthy of popularization and application in medical education, especially for courses involving intricate anatomical components.

#908 The effect of obesity and fat distribution on estimated glomerular filtration rate (eGFR): A Mendelian randomization and image data analysis study

Abstract Background and Aims Obesity is intimately linked to the initiation and progression of chronic kidney disease, but questions on causality still remain. The diagnosis of chronic kidney disease is often based solely on creatinine- and/or cystatin C-based estimated glomerular filtration rate (eGFR), but both these endogenous biomarkers are also known to be related to body composition. This study aims to further investigate how obesity causally affects eGFR, derived from both biomarkers. Method Methods used were: Traditional cross-sectional analyses and magnetic resonance imaging data analyses performed in the POEM study (502 subjects, all aged 50 years), and two-sample Mendelian randomization using already published summary data. Outcome variables for all analyses were eGFR indexed to body surface area, based on creatinine and cystatin C, respectively. In both the traditional cross-sectional and the Mendelian randomization analyses body mass index and waist circumference were used as exposure variables. In the image data analyses, eGFR was regressed nonparametically on fat content and tissue volume for each 3D voxel. Results were visualized as a correlation “imiomics” map. Results Results from the POEM study showed in an adjusted model a negative correlation between cystatin C calculated eGFR and both body mass index [beta = −0.190 (95% CI: −0.280 to −0.100)] and waist circumference [beta = −0.160 (95% CI: −0.250 to −0.060)]. In contrast, a positive association was found for eGFR when using creatinine levels [beta = 0.120 (95% CI: 0.030 to 0.210) for body mass index; beta = 0.160 (95% CI: 0.070 to 0.260) for waist circumference]. This pattern was repeated in the image data analysis, which was visualized in the imiomics map: both visceral and subcutaneous adipose tissue were negatively correlated to eGFR based on cystatin C but positively correlated to eGFR based on creatinine (Fig. 1). Mendelian randomization implied a negative causal effect of obesity-related measures on cystatin C-based eGFR, with statistically significant estimates for both body mass index [beta = −0.031 (95% CI: −0.037 to −0.026)] and waist circumference [beta = −0.038 (95% CI: −0.045 to −0.031)]. No statistically significant effects was found on creatinine-based eGFR (Fig. 2). Conclusion This study indicates a causal negative effect of obesity on cystatin C-based eGFR, but not on eGFR calculated from creatinine. This discrepancy is further highlighted by analyses of adipose tissue from magnetic resonance imaging. These findings thus warrant further research on the impact of using the endogenous biomarkers creatinine and cystatin C to estimate renal function when assessing the link between obesity and chronic kidney disease.

Validation of Traditional Species Identification Techniques for Juvenile Mangrove Crabs (Scylla spp.)

Species identification of mangrove crabs at the juvenile stages is crucial for maximizing production. Traditional knowledge in identifying juvenile mangrove crabs (Scylla spp.) is mainstream although the techniques vary with locations and are yet to be validated for their effectiveness. In this study, widespread traditional techniques for identifying juvenile Scylla were validated by looking for patterns through principal component analysis in dorsal carapace features – including bumps, black spots, coloration, and shape – as determined by CL:ICW. The less prominent bumps on the dorsal carapace and the lower CL:ICW of S. olivacea were seen to have the potential for nationwide adoption to distinguish this least preferred species. The dorsal carapace color varies across species but is inconsistent across regions. The presence of black and white spots, texture features such as depression on the center of the dorsal carapace, and growth rates of the samples were found to have no diagnostic applications for juvenile Scylla. This study confirmed the utility of some traditional techniques to identify juvenile S. olivacea without the aid of a device, but no consistent method to differentiate S. serrata from S. tranquebarica was found. Image analysis data on these validated techniques can be added to the machine learning algorithm of existing species identification tools to increase accuracy.

Grey Matter Atrophy and its Relationship with White Matter Lesions in Patients with Myelin Oligodendrocyte Glycoprotein Antibody-associated Disease, Aquaporin-4 Antibody-Positive Neuromyelitis Optica Spectrum Disorder, and Multiple Sclerosis.

To evaluate: (1) the distribution of gray matter (GM) atrophy in myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), aquaporin-4 antibody-positive neuromyelitis optica spectrum disorder (AQP4+NMOSD), and relapsing-remitting multiple sclerosis (RRMS); and (2) the relationship between GM volumes and white matter lesions in various brain regions within each disease. A retrospective, multicenter analysis of magnetic resonance imaging data included patients with MOGAD/AQP4+NMOSD/RRMS in non-acute disease stage. Voxel-wise analyses and general linear models were used to evaluate the relevance of regional GM atrophy. For significant results (p < 0.05), volumes of atrophic areas are reported. We studied 135 MOGAD patients, 135 AQP4+NMOSD, 175 RRMS, and 144 healthy controls (HC). Compared with HC, MOGAD showed lower GM volumes in the temporal lobes, deep GM, insula, and cingulate cortex (75.79 cm3); AQP4+NMOSD in the occipital cortex (32.83 cm3); and RRMS diffusely in the GM (260.61 cm3). MOGAD showed more pronounced temporal cortex atrophy than RRMS (6.71 cm3), whereas AQP4+NMOSD displayed greater occipital cortex atrophy than RRMS (19.82 cm3). RRMS demonstrated more pronounced deep GM atrophy in comparison with MOGAD (27.90 cm3) and AQP4+NMOSD (47.04 cm3). In MOGAD, higher periventricular and cortical/juxtacortical lesions were linked to reduced temporal cortex, deep GM, and insula volumes. In RRMS, the diffuse GM atrophy was associated with lesions in all locations. AQP4+NMOSD showed no lesion/GM volume correlation. GM atrophy is more widespread in RRMS compared with the other two conditions. MOGAD primarily affects the temporal cortex, whereas AQP4+NMOSD mainly involves the occipital cortex. In MOGAD and RRMS, lesion-related tract degeneration is associated with atrophy, but this link is absent in AQP4+NMOSD. ANN NEUROL 2024;96:276-288.

Digital reference object toolkit of breast DCE MRI for quantitative evaluation of image reconstruction and analysis methods.

To develop a digital reference object (DRO) toolkit to generate realistic breast DCE-MRI data for quantitative assessment of image reconstruction and data analysis methods. A simulation framework in a form of DRO toolkit has been developed using the ultrafast and conventional breast DCE-MRI data of 53 women with malignant (n = 25) or benign (n = 28) lesions. We segmented five anatomical regions and performed pharmacokinetic analysis to determine the ranges of pharmacokinetic parameters for each segmented region. A database of the segmentations and their pharmacokinetic parameters is included in the DRO toolkit that can generate a large number of realistic breast DCE-MRI data. We provide two potential examples for our DRO toolkit: assessing the accuracy of an image reconstruction method using undersampled simulated radial k-space data and assessing the impact of the field inhomogeneity on estimated parameters. The estimated pharmacokinetic parameters for each region showed agreement with previously reported values. For the assessment of the reconstruction method, it was found that the temporal regularization resulted in significant underestimation of estimated parameters by up to 57% and 10% with the weighting factor λ = 0.1 and 0.01, respectively. We also demonstrated that spatial discrepancy of and increase to about 33% and 51% without correction for field. We have developed a DRO toolkit that includes realistic morphology of tumor lesions along with the expected pharmacokinetic parameter ranges. This simulation framework can generate many images for quantitative assessment of DCE-MRI reconstruction and analysis methods.

Enhanced Segmentation of Ischemic Stroke Lesion in MRI Images Using a Geometrically Customised Deep Convolution Model (GCDCM)

Ischemic stroke lesion often known as a stroke, is a significant health issue that requires accurate analysis and classification of brain magnetic resonance imaging (MRI) data. In this study, we propose a novel deep transfer learning approach, called geometrically customized deep convolution model, for the purpose of MRI analysis and classification of brain stroke. Neurostroke segmentation is a serious medical image processing challenge. Segmented regions aid disease identification and treatment. Anywhere can form thrombi. Segmentation facilitates automatic detection because they can be any size or shape. Popular image analysis tool MRI diagnoses well. This diagnostic method shows brain stroke architecture. MRI must replace manual detection. Online datasets recommended cerebral stroke detection and segmentation. Deep learning model MRI scans and detectron 2 with masked CNN Nets segment thrombus. This net architecture recognises dataset stroke boundaries. Classifying strokes with vgg16, resnet50, inceptionv3, and resnet5 transfer learning is possible. Mask the image, then binary predict by eliminating the skull, extracting features, and iterating to find stroke. The model and thrombus mask are predicted if the binary prediction matches the human forecast. Otherwise, data processing resumes. Binary prediction uses the segmentation region and pixels overlap between the ground truth and predicted segmentation to calculate parameters. Compared to reality, the categorization of medical images with weak signals seems tough, especially with a short "train" dataset. Mixing deep learning architectures avoids these drawbacks and extracts signals to accurately classify classes. Deep neural networks best recognise, find, and divide computer vision objects for clinical image analysis. Preprocessing MRI scans, skull stripping with deep CNN architecture combinational net, and brain stroke segmentation are our main tasks. Modern medical image processing is hard. Flexible and uneven borders make brain strokes hard to identify and segment. The transfer learning-based super pixel approach segments brainstrokes. Because we predict every visual pixel, dense prediction occurs. Early discovery of thrombus improves treatment and survival. These procedures have considerably improved our quality indexes.

Deep learning on spatiotemporal graphs: A systematic review, methodological landscape, and research opportunities

Deep learning approaches, given their low cost and high reliability, have gained much popularity in different subjects, such as computer vision and natural language processing, and more recently in graph data types. Spatiotemporal graph-based neural networks have been more and more developed to solve problems related to spatiotemporal data, mainly for analyzing changes over time and for provisioning purposes. In this systematic literature review, we have aimed to answer the most important questions regarding spatiotemporal graph deep learning architectures in different applications domains, such as traffic related topics, medical imaging, and geographical data analysis. We have selected more than 50 papers that cover a wide range of applications and very different architectures and innovations. We have also noticed that most of them consider the spatiotemporal graphs to be quite classic graphs without any further sophisticated modeling of temporal and spatial data evolution. Moreover, core problems (such as node classification, frequent pattern recognition, etc.) and application domains are not sufficiently addressed by the state-of-the-art. This study thus opens many perspectives to new developments in spatio-temporal graph deep learning with different strategies in order to solve various end-to-end tasks and other problems related to this special kind of graph.

Quality Inspection and Problem Analysis of Satellite Image Data in Land Use Survey

Abstract. In order to improve the data quality of land use remote sensing monitoring images, this article introduces the process of generating satellite image data, elaborates on the content of satellite image data verification in land use remote sensing monitoring, and proposes quality issues and improvement measures for satellite image data. Taking the discovered satellite image data quality issues as an example, compared with quality inspection standards, it was found that the main problems in the results were projection parameter errors, image color distortion, image blurring, and position accuracy exceeding limits. It is recommended to check the above issues during the image production stage, analyze the reasons for exceeding the position accuracy limit, image distortion, and embossing, and provide relevant suggestions. Provided strong technical support for land use surveys.

Implementation of Artificial Intelligence in Personalized Prognostic Assessment of Lung Cancer: A Narrative Review.

Artificial Intelligence (AI) has revolutionized the management of non-small-cell lung cancer (NSCLC) by enhancing different aspects, including staging, prognosis assessment, treatment prediction, response evaluation, recurrence/prognosis prediction, and personalized prognostic assessment. AI algorithms may accurately classify NSCLC stages using machine learning techniques and deep imaging data analysis. This could potentially improve precision and efficiency in staging, facilitating personalized treatment decisions. Furthermore, there are data suggesting the potential application of AI-based models in predicting prognosis in terms of survival rates and disease progression by integrating clinical, imaging and molecular data. In the present narrative review, we will analyze the preliminary studies reporting on how AI algorithms could predict responses to various treatment modalities, such as surgery, radiotherapy, chemotherapy, targeted therapy, and immunotherapy. There is robust evidence suggesting that AI also plays a crucial role in predicting the likelihood of tumor recurrence after surgery and the pattern of failure, which has significant implications for tailoring adjuvant treatments. The successful implementation of AI in personalized prognostic assessment requires the integration of different data sources, including clinical, molecular, and imaging data. Machine learning (ML) and deep learning (DL) techniques enable AI models to analyze these data and generate personalized prognostic predictions, allowing for a precise and individualized approach to patient care. However, challenges relating to data quality, interpretability, and the ability of AI models to generalize need to be addressed. Collaboration among clinicians, data scientists, and regulators is critical for the responsible implementation of AI and for maximizing its benefits in providing a more personalized prognostic assessment. Continued research, validation, and collaboration are essential to fully exploit the potential of AI in NSCLC management and improve patient outcomes. Herein, we have summarized the state of the art of applications of AI in lung cancer for predicting staging, prognosis, and pattern of recurrence after treatment in order to provide to the readers a large comprehensive overview of this challenging issue.

Kimmerle anomaly as a cause of vertebrobasillary insufficiency and vertebral pain syndrome

The Kimmerle anomaly is one of the varieties of craniovertebral junction anomalies. Currently, it is one of the main causes of vertebrobasilar insufficiency, vertebral pain and vegetative syndromes. Despite a sufficient number of publications on this problem, the Kimmerle anomaly still remains not fully understood. Insufficient awareness of primary care physicians about this pathology, a wide variety of clinical manifestations, the absence of pathognomonic neurological syndromes, the complexity of the analysis of imaging data cause difficulties in identifying this anomaly. The article describes the epidemiology, pathogenesis, and clinical picture of Kimmerle anomaly. Further, the paper presents modern methods of its diagnosis (spondylography, ultrasound, MRI, MSCT, etc.) and treatment. In conclusion, the authors present ways to optimize the diagnosis and treatment of Kimmerle anomaly.

Attention LSTM U-Net model for Drosophila melanogaster heart tube segmentation in optical coherence microscopy images.

Optical coherence microscopy (OCM) imaging of the Drosophila melanogaster (fruit fly) heart tube has enabled the non-invasive characterization of fly heart physiology in vivo. OCM generates large volumes of data, making it necessary to automate image analysis. Deep-learning-based neural network models have been developed to improve the efficiency of fly heart image segmentation. However, image artifacts caused by sample motion or reflections reduce the accuracy of the analysis. To improve the precision and efficiency of image data analysis, we developed an Attention LSTM U-Net model (FlyNet3.0), which incorporates an attention learning mechanism to track the beating fly heart in OCM images. The new model has improved the intersection over union (IOU) compared to FlyNet2.0 + with reflection artifacts from 86% to 89% and with movement from 81% to 89%. We also extended the capabilities of OCM analysis through the introduction of an automated, in vivo heart wall thickness measurement method, which has been validated on a Drosophila model of cardiac hypertrophy. This work will enable the comprehensive, non-invasive characterization of fly heart physiology in a high-throughput manner.

Balancing Anterior and Posterior Cruciate Ligaments in Adults.

The anterior cruciate ligament (ACL) and posterior cruciate ligament (PCL) represent the central pivot of the knee. The balance between these two ligaments impacts the tibiofemoral biomechanics. Each structure is the opposite of the other in terms of anteroposterior translation and rotation. The aimof this study was to find a correlation between the cross-sectional area of the ACL and PCL in adults. Magnetic resonance imaging (MRI) data analysis was conducted by a musculoskeletal radiologist using MRI planes tailored to the study's requirements. In all 62 studied patients, measurements were done according to the protocol. The study observed three types of intercondylar notches: Type U was identified in 35% (22) of patients, type W in 27% (17), and type A in 37% (23). The median difference between the ACL and PCL areas was found to be statistically significant (p = 0.02). A significant difference in the area of the ACL was detected between Type A and Type U notches (p = 0.02), while no significant differences were found between Type A-W and Type W-U after post hoc corrections (p > 0.05). Additionally, no significant difference was observed in the mean area of the PCL across all three notch types (p = 0.1). In 68% of the cases, the ACL is no less than 60% of the PCL in area, and no more than 120%. The size of ACL and PCL in healthy individuals also depends on other factors like synergistic and antagonistic muscle activities, occupation, and the hip-knee-ankle axis. For example, if the PCL area is 0.79 cm² and the measured structure is round (during a reconstruction a hamstring graft is round), the diameter is 10 mm. A native ACL is, in 68% of the cases, no less than 7.7 mm, and no more than 10.9 mm. The ACL-PCL size correlation helps in understanding the balance of the central pivot of the knee.

Advancements in Deep Learning for Minimally Invasive Surgery: A Journey through Surgical System Evolution

The surge in artificial intelligence (AI) applications across diverse fields owes much to advancements in deep learning and computational processing speed. In medicine, AI's reach extends to medical image analysis and genomic data interpretation. More recently, AI's role in analyzing minimally invasive surgery (MIS) videos has gained traction, with a growing body of research focusing on organ and anatomy identification, instrument recognition, procedure recognition, surgical phase delineation, surgery duration prediction, optimal incision line identification, and surgical education. Concurrently, the development of autonomous surgical robots, exemplified by the Smart Tissue Autonomous Robot (STAR) and RAVEN systems, has shown promising strides. Notably, STAR is currently employed in laparoscopic imaging to discern the surgical site from laparoscopic images and is undergoing trials for an automated suturing system, albeit in animal models. This review contemplates the prospect of fully autonomous surgical robots in the future.