Diagnosis of placenta accreta spectrum using ultrasound texture feature fusion and machine learning.

INTRODUCTION: Placenta accreta spectrum (PAS) is an obstetric disorder arising from the abnormal adherence of the placenta to the uterine wall, often leading to life-threatening complications including postpartum hemorrhage. Despite its significance, PAS remains frequently underdiagnosed before delivery. This study delves into the realm of machine learning to enhance the precision of PAS classification. We introduce two distinct models for PAS classification employing ultrasound texture features. METHODS: The first model leverages machine learning techniques, harnessing texture features extracted from ultrasound scans. The second model adopts a linear classifier, utilizing integrated features derived from 'weighted z-scores'. A novel aspect of our approach is the amalgamation of classical machine learning and statistical-based methods for feature selection. This, coupled with a more transparent classification model based on quantitative image features, results in superior performance compared to conventional machine learning approaches. RESULTS: Our linear classifier and machine learning models attain test accuracies of 87 % and 92 %, and 5-fold cross validation accuracies of 88.7 (4.4) and 83.0 (5.0), respectively. CONCLUSIONS: The proposed models illustrate the effectiveness of practical and robust tools for enhanced PAS detection, offering non-invasive and computationally-efficient diagnostic tools. As adjunct methods for prenatal diagnosis, these tools can assist clinicians by reducing the need for unnecessary interventions and enabling earlier planning of management strategies for delivery.

Scoping review of magnetic resonance motion imaging phantoms.

To review and analyze the currently available MRI motion phantoms. Publications were collected from the Toronto Metropolitan University Library, PubMed, and IEEE Xplore. Phantoms were categorized based on the motions they generated: linear/cartesian, cardiac-dilative, lung-dilative, rotational, deformation or rolling. Metrics were extracted from each publication to assess the motion mechanisms, construction methods, as well as phantom validation. A total of 60 publications were reviewed, identifying 48 unique motion phantoms. Translational movement was the most common movement (used in 38% of phantoms), followed by cardiac-dilative (27%) movement and rotational movement (23%). The average degrees of freedom for all phantoms were determined to be 1.42. Motion phantom publications lack quantification of their impact on signal-to-noise ratio through standardized testing. At present, there is a lack of phantoms that are designed for multi-role as many currently have few degrees of freedom.

Effect of Injection Parameters on the MRI and Dielectric Properties of Condensation-Cured Silicone.

Phantoms with tissue-mimicking properties play a crucial role in the calibration of medical imaging modalities, including Magnetic Resonance Imaging (MRI). Among these phantoms, silicone-based ones are widely used due to their long-term stability in MR imaging. Most of these phantoms are manufactured using traditional pour-mold techniques which often result in the production of air bubbles that can damage the phantom. This research investigates the feasibility of utilizing extrusion techniques to fabricate silicone phantoms and explores the effects of extrusion parameters including plunger speed and nozzle diameter on void content, T1 and T2 relaxation times, and dielectric properties. A custom double-syringe silicone extrusion apparatus was developed to prepare the silicone samples. The void content, relaxometry, and dielectric properties of extruded samples were measured and compared with traditional poured samples. The results show that extrusion parameters can affect the void content of the silicone samples. The presence of voids in the samples resulted in lower T1 values, indicating an inverse relationship between void content and relaxometry. This study demonstrates the potential of extrusion techniques for manufacturing silicone phantoms with reduced air bubble formation and provides valuable insights into the relationship between extrusion parameters and phantom properties.

Predicting adverse outcomes in pregnant patients positive for SARS-CoV-2: a machine learning approach- a retrospective cohort study.

BACKGROUND: Pregnant people are particularly vulnerable to SARS-CoV-2 infection and to ensuing severe illness. Predicting adverse maternal and perinatal outcomes could aid clinicians in deciding on hospital admission and early initiation of treatment in affected individuals, streamlining the triaging processes. METHODS: An international repository of 1501 SARS-CoV-2-positive cases in pregnancy was created, consisting of demographic variables, patient comorbidities, laboratory markers, respiratory parameters, and COVID-19-related symptoms. Data were filtered, preprocessed, and feature selection methods were used to obtain the optimal feature subset for training a variety of machine learning models to predict maternal or fetal/neonatal death or critical illness. RESULTS: The Random Forest model demonstrated the best performance among the trained models, correctly identifying 83.3% of the high-risk patients and 92.5% of the low-risk patients, with an overall accuracy of 89.0%, an AUC of 0.90 (95% Confidence Interval 0.83 to 0.95), and a recall, precision, and F1 score of 0.85, 0.94, and 0.89, respectively. This was achieved using a feature subset of 25 features containing patient characteristics, symptoms, clinical signs, and laboratory markers. These included maternal BMI, gravidity, parity, existence of pre-existing conditions, nicotine exposure, anti-hypertensive medication administration, fetal malformations, antenatal corticosteroid administration, presence of dyspnea, sore throat, fever, fatigue, duration of symptom phase, existence of COVID-19-related pneumonia, need for maternal oxygen administration, disease-related inpatient treatment, and lab markers including sFLT-1/PlGF ratio, platelet count, and LDH. CONCLUSIONS: We present the first COVID-19 prognostication pipeline specifically for pregnant patients while utilizing a large SARS-CoV-2 in pregnancy data repository. Our model accurately identifies those at risk of severe illness or clinical deterioration, presenting a promising tool for advancing personalized medicine in pregnant patients with COVID-19.

AFNet Algorithm for Automatic Amniotic Fluid Segmentation from Fetal MRI.

Amniotic Fluid Volume (AFV) is a crucial fetal biomarker when diagnosing specific fetal abnormalities. This study proposes a novel Convolutional Neural Network (CNN) model, AFNet, for segmenting amniotic fluid (AF) to facilitate clinical AFV evaluation. AFNet was trained and tested on a manually segmented and radiologist-validated AF dataset. AFNet outperforms ResUNet++ by using efficient feature mapping in the attention block and transposing convolutions in the decoder. Our experimental results show that AFNet achieved a mean Intersection over Union (mIoU) of 93.38% on our dataset, thereby outperforming other state-of-the-art models. While AFNet achieves performance scores similar to those of the UNet++ model, it does so while utilizing merely less than half the number of parameters. By creating a detailed AF dataset with an improved CNN architecture, we enable the quantification of AFV in clinical practice, which can aid in diagnosing AF disorders during gestation.

Fet-Net Algorithm for Automatic Detection of Fetal Orientation in Fetal MRI.

Identifying fetal orientation is essential for determining the mode of delivery and for sequence planning in fetal magnetic resonance imaging (MRI). This manuscript describes a deep learning algorithm named Fet-Net, composed of convolutional neural networks (CNNs), which allows for the automatic detection of fetal orientation from a two-dimensional (2D) MRI slice. The architecture consists of four convolutional layers, which feed into a simple artificial neural network. Compared with eleven other prominent CNNs (different versions of ResNet, VGG, Xception, and Inception), Fet-Net has fewer architectural layers and parameters. From 144 3D MRI datasets indicative of vertex, breech, oblique and transverse fetal orientations, 6120 2D MRI slices were extracted to train, validate and test Fet-Net. Despite its simpler architecture, Fet-Net demonstrated an average accuracy and F1 score of 97.68% and a loss of 0.06828 on the 6120 2D MRI slices during a 5-fold cross-validation experiment. This architecture outperformed all eleven prominent architectures (p < 0.05). An ablation study proved each component's statistical significance and contribution to Fet-Net's performance. Fet-Net demonstrated robustness in classification accuracy even when noise was introduced to the images, outperforming eight of the 11 prominent architectures. Fet-Net's ability to automatically detect fetal orientation can profoundly decrease the time required for fetal MRI acquisition.

Automatic Artifact Detection Algorithm in Fetal MRI.

Fetal MR imaging is subject to artifacts including motion, chemical shift, and radiofrequency artifacts. Currently, such artifacts are detected by the MRI operator, a process which is subjective, time consuming, and prone to errors. We propose a novel algorithm, RISE-Net, that can consistently, automatically, and objectively detect artifacts in 3D fetal MRI. It makes use of a CNN ensemble approach where the first CNN aims to identify and classify any artifacts in the image, and the second CNN uses regression to determine the severity of the detected artifacts. The main mechanism in RISE-Net is the stacked Residual, Inception, Squeeze and Excitation (RISE) blocks. This classification network achieved an accuracy of 90.34% and a F1 score of 90.39% and outperformed other state-of-the-art architectures, such as VGG-16, Inception, ResNet-50, ReNet-Inception, SE-ResNet, and SE-Inception. The severity regression network had an MSE of 0.083 across all classes. The presented algorithm facilitates rapid and accurate fetal MRI quality assurance that can be implemented into clinical use.

Fetal Organ Anomaly Classification Network for Identifying Organ Anomalies in Fetal MRI.

Rapid development in Magnetic Resonance Imaging (MRI) has played a key role in prenatal diagnosis over the last few years. Deep learning (DL) architectures can facilitate the process of anomaly detection and affected-organ classification, making diagnosis more accurate and observer-independent. We propose a novel DL image classification architecture, Fetal Organ Anomaly Classification Network (FOAC-Net), which uses squeeze-and-excitation (SE) and naïve inception (NI) modules to automatically identify anomalies in fetal organs. This architecture can identify normal fetal anatomy, as well as detect anomalies present in the (1) brain, (2) spinal cord, and (3) heart. In this retrospective study, we included fetal 3-dimensional (3D) SSFP sequences of 36 participants. We classified the images on a slice-by-slice basis. FOAC-Net achieved a classification accuracy of 85.06, 85.27, 89.29, and 82.20% when predicting brain anomalies, no anomalies (normal), spinal cord anomalies, and heart anomalies, respectively. In a comparison study, FOAC-Net outperformed other state-of-the-art classification architectures in terms of class-average F1 and accuracy. This work aims to develop a novel classification architecture identifying the affected organs in fetal MRI.

Anthropomorphic brain phantoms for use in MRI systems: a systematic review.

OBJECTIVE: To provide a systematic review of available brain MRI phantoms for comparison of structural and functional characteristics. MATERIALS AND METHODS: Phantoms were identified from a literature search using two databases including Google Scholar and PubMed. Narrow inclusion criteria were followed for identification of only tissue-mimicking MRI phantoms excluding digital, computational, or numerical phantoms. Assessment criteria for the identified phantoms was based on three categories being anatomical accuracy, tissue-mimicking materials, and exhibiting relaxation times approximating in-vivo tissues. The available features and uses of each phantom were reported and discussed using the assessment criteria. RESULTS: Ten phantoms were identified after screening; each proposed phantom was then summarized in a table (Table 2). Significant features and characteristics were shown in the comparisons of phantom type in each category, being anthropomorphic vs. traditional phantoms. Anthropomorphic phantoms had more anatomically accurate features than traditional phantoms. On the other hand, traditional phantoms commonly used effective tissue-mimicking materials and accurate electromagnetic properties. DISCUSSION: The findings provide an overview of the different proposed tissue-mimicking MRI brain phantoms available. Various uses and features are highlighted by comparing criteria such as anatomical accuracy, tissue-mimicking material, and electromagnetic properties. Tissue-mimicking MRI phantoms are an extremely useful tool for researchers and clinicians. Future applications include personalized phantom technology and validation of MR imaging and segmentation methods.

Artificial Intelligence and Surgical Education: A Systematic Scoping Review of Interventions.

OBJECTIVE: To synthesize peer-reviewed evidence related to the use of artificial intelligence (AI) in surgical education DESIGN: We conducted and reported a scoping review according to the standards outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analysis with extension for Scoping Reviews guideline and the fourth edition of the Joanna Briggs Institute Reviewer's Manual. We systematically searched eight interdisciplinary databases including MEDLINE-Ovid, ERIC, EMBASE, CINAHL, Web of Science: Core Collection, Compendex, Scopus, and IEEE Xplore. Databases were searched from inception until the date of search on April 13, 2021. SETTING/PARTICIPANTS: We only examined original, peer-reviewed interventional studies that self-described as AI interventions, focused on medical education, and were relevant to surgical trainees (defined as medical or dental students, postgraduate residents, or surgical fellows) within the title and abstract (see Table 2). Animal, cadaveric, and in vivo studies were not eligible for inclusion. RESULTS: After systematically searching eight databases and 4255 citations, our scoping review identified 49 studies relevant to artificial intelligence in surgical education. We found diverse interventions related to the evaluation of surgical competency, personalization of surgical education, and improvement of surgical education materials across surgical specialties. Many studies used existing surgical education materials, such as the Objective Structured Assessment of Technical Skills framework or the JHU-ISI Gesture and Skill Assessment Working Set database. Though most studies did not provide outcomes related to the implementation in medical schools (such as cost-effective analyses or trainee feedback), there are numerous promising interventions. In particular, many studies noted high accuracy in the objective characterization of surgical skill sets. These interventions could be further used to identify at-risk surgical trainees or evaluate teaching methods. CONCLUSIONS: There are promising applications for AI in surgical education, particularly for the assessment of surgical competencies, though further evidence is needed regarding implementation and applicability.

Artificial amniotic fluid for nuclear magnetic resonance spectroscopy studies.

Amniocentesis is the process of retrieving the nutrient-rich amniotic fluid (AF) that encompasses the growing fetus in order to diagnose fetal diseases and developmental disorders. Currently, it is only performed on pregnant persons at risk and is invasive with the potential for infection and in some cases, miscarriage. A non-invasive alternative is needed and could be developed using magnetic resonance spectroscopy (MRS). To develop such MRS sequences, ample testing and training are needed and could be most efficiently conducted on a phantom. We propose a protocol for creating such a synthetic AF phantom for MRS testing and optimization. The proposed AF is validated using nuclear magnetic resonance (NMR) proving it produces spectra comparable to those in the literature. The results from this study can aid in developing a non-invasive fetal diagnostic tool to replace amniocentesis.

Medical Augmentation (Med-Aug) for Optimal Data Augmentation in Medical Deep Learning Networks.

Deep learning (DL) algorithms have become an increasingly popular choice for image classification and segmentation tasks; however, their range of applications can be limited. Their limitation stems from them requiring ample data to achieve high performance and adequate generalizability. In the case of clinical imaging data, images are not always available in large quantities. This issue can be alleviated by using data augmentation (DA) techniques. The choice of DA is important because poor selection can possibly hinder the performance of a DL algorithm. We propose a DA policy search algorithm that offers an extended set of transformations that accommodate the variations in biomedical imaging datasets. The algorithm makes use of the efficient and high-dimensional optimizer Bi-Population Covariance Matrix Adaptation Evolution Strategy (BIPOP-CMA-ES) and returns an optimal DA policy based on any input imaging dataset and a DL algorithm. Our proposed algorithm, Medical Augmentation (Med-Aug), can be implemented by other researchers in related medical DL applications to improve their model's performance. Furthermore, we present our found optimal DA policies for a variety of medical datasets and popular segmentation networks for other researchers to use in related tasks.

Cross Attention Squeeze Excitation Network (CASE-Net) for Whole Body Fetal MRI Segmentation.

Segmentation of the fetus from 2-dimensional (2D) magnetic resonance imaging (MRI) can aid radiologists with clinical decision making for disease diagnosis. Machine learning can facilitate this process of automatic segmentation, making diagnosis more accurate and user independent. We propose a deep learning (DL) framework for 2D fetal MRI segmentation using a Cross Attention Squeeze Excitation Network (CASE-Net) for research and clinical applications. CASE-Net is an end-to-end segmentation architecture with relevant modules that are evidence based. The goal of CASE-Net is to emphasize localization of contextual information that is relevant in biomedical segmentation, by combining attention mechanisms with squeeze-and-excitation (SE) blocks. This is a retrospective study with 34 patients. Our experiments have shown that our proposed CASE-Net achieved the highest segmentation Dice score of 87.36%, outperforming other competitive segmentation architectures.

Characterization of Mechanical and Dielectric Properties of Silicone Rubber.

Silicone rubber's silicone-oxygen backbones give unique material properties which are applicable in various biomedical devices. Due to the diversity of potential silicone rubber compositions, the material properties can vary widely. This paper characterizes the dielectric and mechanical properties of two different silicone rubbers, each with a different cure system, and in combination with silicone additives. A tactile mutator (Slacker™) and/or silicone thickener (Thi-vex™) were mixed with platinum-cured and condensation-cured silicone rubber in various concentrations. The dielectric constants, conductivities, and compressive and shear moduli were measured for each sample. Our study contributes novel information about the dielectric and mechanical properties of these two types of silicone rubber and how they change with the addition of two common silicone additives.

Quantitative analysis of fetal magnetic resonance phantoms and recommendations for an anthropomorphic motion phantom.

OBJECTIVE: To provide a review and quantitative analysis of the available fetal MR imaging phantoms. MATERIALS AND METHODS: A literature search was conducted across Pubmed, Google Scholar, and Ryerson University Library databases to identify fetal MR imaging phantoms. Phantoms were graded on a semi-quantitative scale in regards to four evaluation categories: (1) anatomical accuracy in size and shape, (2) dielectric conductivity similar to the simulated tissue, (3) relaxation times similar to simulated tissue, and (4) physiological motion similar to fetal gross body, cardiovascular, and breathing motion. This was followed by statistical analysis to identify significant findings. RESULTS: Seventeen fetal phantoms were identified and had an average overall percentage accuracy of 26%, with anatomical accuracy being satisfied the most (56%) and physiological motion the least (7%). Phantoms constructed using 3D printing were significantly more accurate than conventionally constructed phantoms. DISCUSSION: Currently available fetal phantoms lack accuracy and motion simulation. 3D printing may lead to higher accuracy compared with traditional manufacturing. Future research needs to focus on properly simulating both fetal anatomy and physiological motion to produce a phantom that is appropriate for fetal MRI sequence development and optimization.

Uterine artery and umbilical vein blood flow are unaffected by moderate habitual physical activity during pregnancy.

OBJECTIVE: This study aims to noninvasively quantify blood flow in the uterine arteries (UTAs) and umbilical vein (UV) using phase-contrast magnetic resonance imaging (PC-MRI) and test whether these correlate with maternal fitness parameters. METHOD: Resting UTA and UV flows were measured in 23 healthy 30 ± 3-year-old women who engaged in moderate-intensity physical activity during pregnancy. Participant fitness was characterized in the second and third trimesters using the submaximal oxygen uptake (VO2 ) test measuring heart rate (HR), VO2 , ventilation (ventilatory equivalent [VE]/VO2 ), and the Borg rating of perceived exertion (respiratory quotient [RQ]). Linear regression models were used to determine the associations between blood flow and maternal fitness measures. RESULTS: Blood flows in the UTA (957 ± 241 mL/min) and UV (132 ± 38 mL/min/kg) were successfully measured in 20 (87%) participants. Neither was associated with any physical fitness parameters (HR, VO2 , VE/VO2 , and RQ) nor with any second-to-third trimester change in these parameters. CONCLUSION: PC-MRI can be used to noninvasively measure blood flow in the UTA and UV. Neither resting UTA nor UV flow is associated with maternal fitness parameters. This is the first MRI-based study to provide novel hemodynamic data suggesting decoupling between maternal moderate fitness level and the maternal-placental-fetal hemodynamic system in healthy, normal body mass index (BMI) pregnancies.

Multidimensional fetal flow imaging with cardiovascular magnetic resonance: a feasibility study.

PURPOSE: To image multidimensional flow in fetuses using golden-angle radial phase contrast cardiovascular magnetic resonance (PC-CMR) with motion correction and retrospective gating. METHODS: A novel PC-CMR method was developed using an ungated golden-angle radial acquisition with continuously incremented velocity encoding. Healthy subjects (n = 5, 27 ± 3 years, males) and pregnant females (n = 5, 34 ± 2 weeks gestation) were imaged at 3 T using the proposed sequence. Real-time reconstructions were first performed for retrospective motion correction and cardiac gating (using metric optimized gating, MOG). CINE reconstructions of multidimensional flow were then performed using the corrected and gated data. RESULTS: In adults, flows obtained using the proposed method agreed strongly with those obtained using a conventionally gated Cartesian acquisition. Across the five adults, bias and limits of agreement were - 1.0 cm/s and [- 5.1, 3.2] cm/s for mean velocities and - 1.1 cm/s and [- 6.5, 4.3] cm/s for peak velocities. Temporal correlation between corresponding waveforms was also high (R~ 0.98). Calculated timing errors between MOG and pulse-gating RR intervals were low (~ 20 ms). First insights into multidimensional fetal blood flows were achieved. Inter-subject consistency in fetal descending aortic flows (n = 3) was strong with an average velocity of 27.1 ± 0.4 cm/s, peak systolic velocity of 70.0 ± 1.8 cm/s and an intra-class correlation coefficient of 0.95 between the velocity waveforms. In one fetal case, high flow waveform reproducibility was demonstrated in the ascending aorta (R = 0.97) and main pulmonary artery (R = 0.99). CONCLUSION: Multidimensional PC-CMR of fetal flow was developed and validated, incorporating retrospective motion compensation and cardiac gating. Using this method, the first quantification and visualization of multidimensional fetal blood flow was achieved using CMR.

Concussion induces focal and widespread neuromorphological changes.

Concussion induces transient, and oftentimes chronic, lingering impairment to mental functioning, which must be driven by some underlying neurobiological perturbation - however, the physical changes related to sequelae are difficult to detect. Previous imaging studies on concussion have focused on alterations to cortical anatomy, but few have examined the cerebrum, subcortex, and cerebellum. Here, we present an analysis of these structures in a single cohort (all males, 21 patients, 22 controls) using MRI and diagnosed with a single-concussive episode in the acute and sub-acute stages of injury. Structural images were segmented into 78 cortical brain regions and 81,924 vertices using the CIVET algorithm. Subcortical volumetric analyses of the cerebellum, thalamus, globus pallidus, caudate and putamen were conducted following segmentation. Participants with concussion were found to have reduced white and grey matter volume, total cortical volume, as well as cortical thinning, primarily in left frontal areas. No differences were observed in the cerebellum or subcortical structures. In conclusion, just a single concussive episode induces measurable changes in brain structure manifesting as diffuse and local patterns of altered neuromorphometry.

Erratum: The developing human brain: age-related changes in cortical, subcortical, and cerebellar anatomy.

[This corrects the article DOI: 10.1002/brb3.457.].