How doctors can lead the way in their communities: Co-creation, the Indian community and organ donation registration.

BACKGROUND AND AIMS: Transplant success rates can increase when organs and tissues are matched within ethnic communities, but how well are the processes around organ donation understood by discrete ethnic communities in Australia? We investigated this in relation to one ethnic group, the Australian-Indian community in Sydney. METHODS: A culturally appropriate survey and dissemination strategy was co-created with Indian community members through an Advisory Panel. Items were informed by a thematic analysis of cultural beliefs shared through the advisory panel discussions and measured awareness and practices associated with organ donation and transplantation and beliefs about organ donation and registration. Donation information was provided at the end. RESULTS: Two hundred and thirty-eight participants completed the survey. Hinduism along with Tamil and Gujarati were the most frequently identified religious and cultural backgrounds. The processes around organ donation were not well known, and Australian Organ Donor Register registration rates were below the national average. Principal component analysis revealed positive, social, medical trust, concerns, and cardiac and brain death belief factors. Doctors played a key role in generating trust in the donation system, decisions about organ donation were embedded in family and community, and family discussion was related to increased registration. Registered participants reported higher scores on medical trust beliefs, which also predicted family discussion. CONCLUSION: The information needed to understand the process of organ donation and registration in Australia is not embedded in this community, highlighting the need for programmes to be tailored to each culturally diverse community rather than culturally diverse communites in general. Doctors and the advisory panels are pivotal in this process.

The frequency and types of resident relocations in Dutch nursing homes: a nationwide cohort study of electronic health record data.

PURPOSE: One third of Dutch nursing home residents relocated at least once during length of stay. Roughly 75 percent were individual relocations and the other 25 percent concerned group relocations. The average yearly number of individual relocations was about 3 times as high in the first 4 months after admission compared with later periods. METHODS: We performed an historical cohort study of pseudonymized registration data from a Dutch electronic health record. We selected data from nursing home residents aged 65 years and older who stayed on a ward for physical impairment or dementia and passed away between 2015 and 2019. Our study sample consisted of 26,060 long-stay nursing home residents from 67 nursing homes in the Netherlands. We examined the number of relocations, trends over time, individual versus group relocations and relocation destinations. RESULTS: We found that approximately one-third of long-stay nursing home residents relocated at least once with an average of 36 relocations per 100 residents per year. Roughly, 75 percent of relocations were individual relocations and 25 percent concerned group relocations. In the first 4 months after admission, the average number of individual relocations per 100 resident per year was about 3 times as often compared to later periods after admission. Most individual relocations were within the same type of care. CONCLUSION: A considerable proportion of Dutch long-stay nursing home residents experienced one or more relocations. Relocations for individual reasons occurred mostly in the first months after admission. Further investigation is warranted to explore which factors lead to relocations.

Evaluation of cumulative dose distributions from external beam radiation therapy using CT-to-CBCT deformable image registration (DIR) for cervical cancer patients.

PURPOSE: To investigate dose differences between the planning CT (pCT) and dose calculated on pre-treatment verification CBCTs using DIR and dose summation for cervical cancer patients. METHODS: Cervical cancer patients treated at our institution with 45 Gy EBRT undergo a pCT and 5 CBCTs, once every five fractions of treatment. A free-form intensity-based DIR in MIM was performed between the pCT and each CBCT using the "Merged CBCT" feature to generate an extended FOV-CBCT (mCBCT). DIR-generated bladder and rectum contours were adjusted by a physician, and dice similarity coefficients (DSC) were calculated. After deformation, the investigated doses were (1) recalculated in Eclipse using original plan parameters (ecD), and (2) deformed from planning dose (pD) using the deformation matrix in MIM (mdD). Dose summation was performed to the first week's mCBCT. Dose distributions were compared for the bladder, rectum, and PTV in terms of percent dose difference, dose volume histograms (DVHs), and gamma analysis between the calculated doses. RESULTS: For the 20 patients, the mean DSC was 0.68 ± 0.17 for bladder and 0.79 ± 0.09 for rectum. Most patients were within 5% of pD for D2cc (19/20), Dmax (17/20), and Dmean (16/20). All patients demonstrated a percent difference > 5% for bladder V45 due to variations in bladder volume from the pCT. D90 showed fewer differences with 19/20 patients within 2% of pD. Gamma rates between pD and ecD averaged 94% for bladder and 94% for rectum, while pD and mdD exhibited slightly better performance for bladder (93%) and lower for rectum (85%). CONCLUSION: Using DIR with weekly CBCT images, the MIM deformed dose (mdD) was found to be in close agreement with the Eclipse calculated dose (ecD). The proposed workflow should be used on a case-by-case basis when the weekly CBCT shows marked difference in organs-at-risk from the planning CT.

MUsculo-Skeleton-Aware (MUSA) deep learning for anatomically guided head-and-neck CT deformable registration.

Deep-learning-based deformable image registration (DL-DIR) has demonstrated improved accuracy compared to time-consuming non-DL methods across various anatomical sites. However, DL-DIR is still challenging in heterogeneous tissue regions with large deformation. In fact, several state-of-the-art DL-DIR methods fail to capture the large, anatomically plausible deformation when tested on head-and-neck computed tomography (CT) images. These results allude to the possibility that such complex head-and-neck deformation may be beyond the capacity of a single network structure or a homogeneous smoothness regularization. To address the challenge of combined multi-scale musculoskeletal motion and soft tissue deformation in the head-and-neck region, we propose a MUsculo-Skeleton-Aware (MUSA) framework to anatomically guide DL-DIR by leveraging the explicit multiresolution strategy and the inhomogeneous deformation constraints between the bony structures and soft tissue. The proposed method decomposes the complex deformation into a bulk posture change and residual fine deformation. It can accommodate both inter- and intra- subject registration. Our results show that the MUSA framework can consistently improve registration accuracy and, more importantly, the plausibility of deformation for various network architectures. The code will be publicly available at https://github.com/HengjieLiu/DIR-MUSA.

An evaluation of practice supervisor training for final-year healthcare students.

Practice supervisors are fundamental in the development and upskilling of the future workforce in health care. This article outlines a new initiative for one higher education institution, which integrated practice supervisor training into the curriculum for third-year students across several healthcare programmes. The training consisted of two pre-recorded sessions, which were delivered by the students' personal tutors. A final face-to-face session was then facilitated, where students were supported to consolidate their learning and build on their knowledge. Evaluation data were collected via an anonymous questionnaire. In total, 352 questionnaires were returned. Data were managed and analysed using descriptive statistics within Excel. The qualitative comments received were collated and analysed using simple thematic framework analysis. Most attendees found the content and the delivery of the programme and their opportunity for discussions to be good, very good or excellent, showing overall that content was positively evaluated by 98.4% of attendees, delivery at 97.4% and opportunity for discussion at 97.9%. The evaluation data results show that practice supervisor preparation can be effectively delivered when located in the final year for undergraduate healthcare students. The importance of the content being interactive and allowing for plenty of opportunities for group discussions has been highlighted by the feedback.

PViT-AIR: Puzzling vision transformer-based affine image registration for multi histopathology and faxitron images of breast tissue.

Breast cancer is a significant global public health concern, with various treatment options available based on tumor characteristics. Pathological examination of excision specimens after surgery provides essential information for treatment decisions. However, the manual selection of representative sections for histological examination is laborious and subjective, leading to potential sampling errors and variability, especially in carcinomas that have been previously treated with chemotherapy. Furthermore, the accurate identification of residual tumors presents significant challenges, emphasizing the need for systematic or assisted methods to address this issue. In order to enable the development of deep-learning algorithms for automated cancer detection on radiology images, it is crucial to perform radiology-pathology registration, which ensures the generation of accurately labeled ground truth data. The alignment of radiology and histopathology images plays a critical role in establishing reliable cancer labels for training deep-learning algorithms on radiology images. However, aligning these images is challenging due to their content and resolution differences, tissue deformation, artifacts, and imprecise correspondence. We present a novel deep learning-based pipeline for the affine registration of faxitron images, the x-ray representations of macrosections of ex-vivo breast tissue, and their corresponding histopathology images of tissue segments. The proposed model combines convolutional neural networks and vision transformers, allowing it to effectively capture both local and global information from the entire tissue macrosection as well as its segments. This integrated approach enables simultaneous registration and stitching of image segments, facilitating segment-to-macrosection registration through a puzzling-based mechanism. To address the limitations of multi-modal ground truth data, we tackle the problem by training the model using synthetic mono-modal data in a weakly supervised manner. The trained model demonstrated successful performance in multi-modal registration, yielding registration results with an average landmark error of 1.51 mm (±2.40), and stitching distance of 1.15 mm (±0.94). The results indicate that the model performs significantly better than existing baselines, including both deep learning-based and iterative models, and it is also approximately 200 times faster than the iterative approach. This work bridges the gap in the current research and clinical workflow and has the potential to improve efficiency and accuracy in breast cancer evaluation and streamline pathology workflow.

Pedigree tracing to determine the origins of the modern Palomino Horse Breeders of America stock-type horse.

Registration numbers for the Palomino Horse Breeders of America (PHBA) have risen with the assistance of new registration guidelines. These changes in guidelines allowed for registration of any horse with a palomino body color, including those with unknown pedigrees. While this promoted registry growth, documenting the breed's orgins from those known pedigrees making up the breed's foundation is important with the introduction of unknown pedigrees. This can be accomplished using pedigree tracing, which is of particular value for color registries as the study of coat color patterns is plagued with misunderstandings due to pedigrees with inaccurate color designations. Thus, the objective of this study was to develop an understanding of the historical origins of the modern PHBA stock-type horse through pedigree tracing. Horses (n = 460) were randomly sampled from published entries from 10 years (2012-2022) of the PHBA World Show Championship Horse Show. The All-Breed Database was utilized to obtain documentation of extended pedigrees that included uninterrupted generation-to-generation inheritance patterns of a cream dilution allele-influenced coat color. Pedigrees were traced back to the earliest recorded relative. We observed six foundation sires contributed to the modern PHBA stock-type horse with the majority (58%) tracing back to a sire named Old Fred. Two identified sires, Yellow Jacket (4%) and Cotton Eyed Joe (3%), have conflicts concerning color designation. While these results assist in documenting the breed's origins, genetic testing is required to accurately identify the cream dilution allele within the horses registered to ensure a phenotype-based solely on the cream dilution allele.

Unsupervised Multi-parametric MRI Registration Using Neural Optimal Transport.

Precise deformable image registration of multi-parametric MRI sequences is necessary for radiologists in order to identify abnormalities and diagnose diseases, such as prostate cancer and lymphoma. Despite recent advances in unsupervised learning-based registration, volumetric medical image registration that requires considering the variety of data distributions is still challenging. To address the problem of multi-parametric MRI sequence data registration, we propose an unsupervised domain-transported registration method, called OTMorph by employing neural optimal transport that learns an optimal transport plan to map different data distributions. We have designed a novel framework composed of a transport module and a registration module: the former transports data distribution from the moving source domain to the fixed target domain, and the latter takes the transported data and provides the deformed moving volume that is aligned with the fixed volume. Through end-to-end learning, our proposed method can effectively learn deformable registration for the volumes in different distributions. Experimental results with abdominal multi-parametric MRI sequence data show that our method has superior performance over around 67-85% in deforming the MRI volumes compared to the existing learning-based methods. Our method is generic in nature and can be used to register inter-/intra-modality images by mapping the different data distributions in network training.

Automated orofacial virtual patient creation: A proof of concept.

OBJECTIVES: To (1) construct a virtual patient (VP) using facial scan, intraoral scan, and low-dose computed tomography scab based on an Artificial intelligence (AI)-approach, (2) quantitatively compare it with AI-refined and semi-automatic registration, and (3) qualitatively evaluate user satisfaction when using virtual patient as a communication tool in clinical practice. MATERIALS AND METHODS: A dataset of 20 facial scans, intraoral scans, and low-dose computed tomography scans was imported into the Virtual Patient Creator platform to create an automated virtual patient. The accuracy of the virtual patients created using different approaches was further analyzed in the Mimics software. The accuracy (% of corrections required), consistency, and time efficiency of the AI-driven virtual patient registration were then compared with the AI-refined and semi-automatic registration (clinical reference). User satisfaction was assessed through a survey of 35 dentists and 25 laypersons who rated the virtual patient's realism and usefulness for treatment planning and communication on a 5-point scale. RESULTS: The accuracy for AI-driven, AI-refined, and semi-automatic registration virtual patient was 85 %, 85 %, and 100 % for the upper and middle thirds of the face, and 30 %, 30 %, and 35 % for the lower third. Registration consistency was 1, 1 and 0.99, and the average time was 26.5, 30.8, and 385 s, respectively (18-fold time reduction with AI). The inferior facial third exhibited the highest registration mismatch between facial scan and computed tomography. User satisfaction with the virtual patient was consistently high among both dentists and laypersons, with most responses indicating very high satisfaction regarding realism and usefulness as a communication tool. CONCLUSION: The AI-driven registration can provide clinically accurate, fast, and consistent virtual patient creation using facial scans, intraoral scans, and low-dose computed tomography scans, enabling interpersonal communication. CLINICAL SIGNIFICANCE: Using AI for automated segmentation and registration of maxillofacial structures leads to clinically efficient and accurate VP creation, opening the doors for its widespread use in diagnosis, treatment planning, and interprofessional and professional-patient communication.

Automatic Landmark Annotation and Measurement of 3D Mandibular Morphology Using Non-Rigid Registration: A Preliminary Exploration and Accuracy Assessment.

This study aimed to develop an automatic methodology for mandibular landmarking and measurement using non-rigid registration as well as analyze the accuracy of automatic landmarking and measurements.Statistical analysis.Digital technology center, tertiary hospital.130 healthy Chinese adults with equal gender distribution, average age 28.2 ± 5.6 years.Four mean shape mesh templates were generated from 100 head CT scans. Following manual indication of landmarks, these templates were applied for automatic landmark annotation and measurements on mandibles from another 30 head CT scans, using non-rigid iterative closest point registration.Differences of landmark coordinates and measurements between automatic and manual annotation were analyzed using mean difference, centroid size, Euclidean distances and intraclass correlation coefficient (ICC), assessing the accuracy and validity of automatic landmark annotation.The majority of automatic landmarks (16/22) did not exhibit consistent displacement to specific direction. ICCs of all landmark coordinates exceed 0.950, with 87.9% larger than 0.990. The average Euclidean distance between manual and automatic landmarks was 2.038 ± 0.947 mm. Most ICCs of linear and angular measurements between manual and automatic annotation (20/26) exceeded 0.900, with the average errors being 1.425 ± 0.973 mm and 2.257 ± 0.649 °, respectively.A novel and efficient method for automatic landmark annotation was established based on non-rigid registration. Its credibility and accuracy in mandibular annotation and measurements were demonstrated.

Reconsidering Public Attitudes Toward Deceased Organ Donation Registration.

Background: The aim of the current study is to understand Americans' attitudes toward deceased organ donation and to assess their level of acceptance on positions toward organ donation. Methods: In summer and winter of 2023, 2 national samples completed measures of attitudes toward donation, reported their registration status, rated positions related to donation, and provided demographic information. Results: Data from 2 samples indicated registration estimates between 50% and 57% and a significant proportion of those unregistered reported reluctance to register. The believability of national registry estimates and ratings of acceptability of 9 organ donation positions differed by registration status. Conclusion: Mass media and motor vehicles interventions seeking to increase registration rates should take into consideration individual attitudes and support toward donation may not be as strong as once thought.

DELR-Net: a network for 3D multimodal medical image registration in more lightweight application scenarios.

PURPOSE: 3D multimodal medical image deformable registration plays a significant role in medical image analysis and diagnosis. However, due to the substantial differences between images of different modalities, registration is challenging and requires high computational costs. Deep learning-based registration methods face these challenges. The primary aim of this paper is to design a 3D multimodal registration network that ensures high-quality registration results while reducing the number of parameters. METHODS: This study designed a Dual-Encoder More Lightweight Registration Network (DELR-Net). DELR-Net is a low-complexity network that integrates Mamba and ConvNet. The State Space Sequence Module and the Dynamic Large Kernel block are used as the main components of the dual encoders, while the Dynamic Feature Fusion block is used as the main component of the decoder. RESULTS: This study conducted experiments on 3D brain MR images and abdominal MR and CT images. Compared to existing registration methods, DELR-Net achieved better registration results while maintaining a lower number of parameters. Additionally, generalization experiments on other modalities showed that DELR-Net has superior generalization capabilities. CONCLUSION: DELR-Net significantly improves the limitations of 3D multimodal medical image deformable registration, achieving better registration performance with fewer parameters.

Method for co-registration of high-resolution specimen PET-CT with histopathology to improve insight into radiotracer distributions.

BACKGROUND: As the spatial resolution of positron emission tomography (PET) scanners improves, understanding of radiotracer distributions in tissues at high resolutions is important. Hence, we propose a method for co-registration of high-resolution ex vivo specimen PET images, combined with computed tomography (CT) images, and the corresponding specimen histopathology. METHODS: We applied our co-registration method to breast cancer (BCa) specimens of patients who were preoperatively injected with 0.8 MBq/kg [ 18 F]fluorodeoxyglucose ([18F]FDG). The method has two components. First, we used an image acquisition scheme that minimises and tracks tissue deformation: (1) We acquired sub-millimetre (micro)-PET-CT images of ±2 mm-thick lamellas of the fresh specimens, enclosed in tissue cassettes. (2) We acquired micro-CT images of the same lamellas after formalin fixation to visualise tissue deformation. (3) We obtained 1 hematoxylin and eosin (H&E) stained histopathology section per lamella of which we captured a digital whole slide image (WSI). Second, we developed an automatic co-registration algorithm to improve the alignment between the micro-PET-CT images and WSIs, guided by the micro-CT of the fixated lamellas. To estimate the spatial co-registration error, we calculated the distance between corresponding microcalcifications in the micro-CTs and WSIs. The co-registered images allowed to study standardised uptake values (SUVs) of different breast tissues, as identified on the WSIs by a pathologist. RESULTS: We imaged 22 BCa specimens, 13 cases of invasive carcinoma of no special type (NST), 6 of invasive lobular carcinoma (ILC), and 3 of ductal carcinoma in situ (DCIS). While the cassette framework minimised tissue deformation, the best alignment between the micro-PET-CT images and WSIs was achieved after deformable co-registration. We found an overall average co-registration error of 0.74 ± 0.17 mm between the micro-PET images and WSIs. (Pre)malignant tissue (including NST, ILC, and DCIS) generally showed higher SUVs than healthy tissue (including healthy glandular, connective, and adipose tissue). As expected, inflamed tissue and skin also showed high uptake. CONCLUSIONS: We developed a method to co-register micro-PET-CT images of surgical specimens and WSIs with an accuracy comparable to the spatial resolution of the micro-PET images. While currently, we only applied this method to BCa specimens, we believe this method is applicable to a wide range of specimens and radiotracers, providing insight into distributions of (new) radiotracers in human malignancies at a sub-millimetre resolution.

Harm effects in non-registered versus registered randomized controlled trials of medications: a retrospective cohort study of clinical trials.

BACKGROUND: Trial registration aims to address potential bias from selective or non-reporting of findings, and therefore has a vital role in promoting transparency and accountability of clinical research. In this study, we aim to investigate the influence of trial registration on estimated harm effects in randomized controlled trials of medication interventions. METHODS: We searched PubMed for systematic reviews and meta-analyses of randomized trials on medication harms indexed between January 1, 2015, and January 1, 2020. To be included in the analyses, eligible meta-analyses should have at least five randomized trials with distinct registration statuses (i.e., prospectively registered, retrospectively registered, and non-registered) and 2 by 2 table data for adverse events for each trial. To control for potential confounding, trials in each meta-analysis were analyzed within confounder-harmonized groups (e.g., dosage) identified using the Directed Acyclic Graph method. The harm estimates arising from the trials with different registration statuses were compared within the confounder-harmonized groups using hierarchical linear regression. Results are shown as ratio of odds ratio (OR) and 95% confidence interval (CI). RESULTS: The dataset consists of 629 meta-analyses of harms with 10,069 trials. Of these trials, 74.3% were registered, and 23.9% were not registered, and for those registered, 70.6% were prospectively registered, while 26.3% were retrospectively registered. In comparison to prospectively registered trials, both non-registered trials (ratio of OR = 0.82, 95%CI 0.68 to 0.98, P = 0.03) and retrospectively registered trials (ratio of OR = 0.75, 95%CI 0.66 to 0.86, P < 0.01) had lower OR for harms based on 69 and 126 confounders-harmonized groups. The OR of harms did not differ between retrospectively registered and non-registered trials (ratio of OR = 1.02, 95%CI 0.85 to 1.23, P = 0.83) based on 76 confounders-harmonized groups. CONCLUSIONS: Medication-related harms may be understated in non-registered trials, and there was no obvious evidence that retrospective registration had a demonstrable benefit in reducing such selective or absent reporting. Prospective registration is highly recommended for future trials.

Augmented Reality Implementation in Minimally Invasive Surgery for Future Application in Pulmonary Surgery: A Systematic Review.

OBJECTIVE: This systematic review investigates of Augmented Reality (AR) systems used in minimally invasive surgery of deformable organs, focusing on initial registration, dynamic tracking, and visualization. The objective is to acquire a comprehensive understanding of the current knowledge, applications, and challenges associated with current AR-techniques, aiming to leverage these insights for developing a dedicated AR pulmonary Video or Robotic Assisted Thoracic Surgery (VATS/RATS) workflow. METHODS: A systematic search was conducted within Embase, Medline (Ovid) and Web of Science on April 16, 2024, following the Preferred Reporting items for Systematic Reviews and Meta-Analyses (PRISMA). The search focused on intraoperative AR applications and intraoperative navigational purposes for deformable organs. Quality assessment was performed and studies were categorized according to initial registration and dynamic tracking methods. RESULTS: 33 articles were included, of which one involved pulmonary surgery. Studies used both manual and (semi-) automatic registration methods, established through anatomical landmark-based, fiducial-based, or surface-based techniques. Diverse outcome measures were considered, including surgical outcomes and registration accuracy. The majority of studies that reached an registration accuracy below 5 mm applied surface-based registration. CONCLUSIONS: AR can potentially aid surgeons with real-time navigation and decision making during anatomically complex minimally invasive procedures. Future research for pulmonary applications should focus on exploring surface-based registration methods, considering their non-invasive, marker-less nature, and promising accuracy. Additionally, vascular-labeling-based methods are worth exploring, given the importance and relative stability of broncho-vascular anatomy in pulmonary VATS/RATS. Assessing clinical feasibility of these approaches is crucial, particularly concerning registration accuracy and potential impact on surgical outcomes.

Mechanisms of gait speed changes in middle-aged adults: Simultaneous analysis of magnitude and temporal effects.

BACKGROUND: Middle-aged adults represent the transition between younger and older adults, where some of the characteristic gait differences due to aging begins to surface. However, the gait characteristics of middle-aged adults across the whole gait cycle remains an understudied topic. As speed is a sensitive indicator of health, characterizing the effects of speed on the gait of middle-aged adults and differentiating it from the response of young adults will provide insights into the effects of aging on gait speed modulation mechanisms. RESEARCH QUESTION: What are the mechanisms of gait speed changes that are employed by middle-aged adults, and how are they different from younger adults? METHODS: A cohort of healthy young and middle-aged adults completed 60 second trials at three different speeds. Joint kinematics, kinetics, and surface electromyography data were analyzed and compared between the speed levels and age groups. Statistical Parametric Mapping along with a nonlinear curve registration algorithm was used to simultaneously assess the changes in both magnitude and timing of different metrics. RESULTS: When compared to the younger cohort, the middle-aged cohort had significantly lower ankle range of motion, dorsiflexion moment during loading response and plantarflexion moment during push-off. At the knee joint, the middle-aged adults had significantly lower knee flexion moment during stance. At the hip joint, the middle-aged adults had lower extension moment during terminal stance. SIGNIFICANCE: Time-continuous analysis showed that primary differences due to age were related to decreased joint range of motion and joint moment production capability in the middle-aged adults. Faster walking appears a safe method for middle-aged adults to increase joint range of motion and joint moment expression. However, targeted interventions that focus on improving capability are likely also needed. Suggested targets being improving ankle and knee joint moment capability, and increased range of motion at all joints.

Genetic Architectures of Medical Images Revealed by Registration of Multiple Modalities.

The advent of biobanks with vast quantities of medical imaging and paired genetic measurements creates huge opportunities for a new generation of genotype-phenotype association studies. However, disentangling biological signals from the many sources of bias and artifacts remains difficult. Using diverse medical images and time-series (ie, magnetic resonance imagings [MRIs], electrocardiograms [ECGs], and dual-energy X-ray absorptiometries [DXAs]), we show how registration, both spatial and temporal, guided by domain knowledge or learned de novo, helps uncover biological information. A multimodal autoencoder comparison framework quantifies and characterizes how registration affects the representations that unsupervised and self-supervised encoders learn. In this study we (1) train autoencoders before and after registration with nine diverse types of medical image, (2) demonstrate how neural network-based methods (VoxelMorph, DeepCycle, and DropFuse) can effectively learn registrations allowing for more flexible and efficient processing than is possible with hand-crafted registration techniques, and (3) conduct exhaustive phenotypic screening, comprised of millions of statistical tests, to quantify how registration affects the generalizability of learned representations. Genome- and phenome-wide association studies (GWAS and PheWAS) uncover significantly more associations with registered modality representations than with equivalently trained and sized representations learned from native coordinate spaces. Specifically, registered PheWAS yielded 61 more disease associations for ECGs, 53 more disease associations for cardiac MRIs, and 10 more disease associations for brain MRIs. Registration also yields significant increases in the coefficient of determination when regressing continuous phenotypes (eg, 0.36 ± 0.01 with ECGs and 0.11 ± 0.02 for DXA scans). Our findings reveal the crucial role registration plays in enhancing the characterization of physiological states across a broad range of medical imaging data types. Importantly, this finding extends to more flexible types of registration, such as the cross-modal and the circular mapping methods presented here.

Beyond the binary female/male sex classification: The impact of (trans)gender on the identification of human remains.

In cases of unidentified deceased persons, sex determination is a routine task in forensic medicine. However, the binary biological sex categories 'female' and 'male' may be challenged if it is not clear whether the information in the missing persons databases refers to the biological sex or the (felt and lived) gender. An umbrella term for people who do not identify with their birth sex (which usually is the biological, chromosomal sex) is 'transgender'. In recent decades, the legal and social situation of transgender people has changed in many countries making it easier to live their felt gender more openly. This development highlights the issue of potential challenges in the postmortem identification of transgender individuals. Serious problems in corresponding cases may be rare-but they must be considered and addressed in forensic practice to minimize the risk of delayed or failed identification. The impact of (trans)gender on the identification of human remains was examined by a narrative literature review under special consideration of the prevalences of transgender identities in general populations and in the group of unidentified deceased; possible actions to avoid problems in the postmortem identification of transgender persons in forensic practice are being proposed. One can assume that 1 of 200 people in the United States, the European Union and comparable societies is transgender with an opposite-sex identification, and 2 to 3 of 100 people live outside the typical female/male binary, with numbers increasing. If legally possible, an increasing number of transgender individuals will change their name and gender in civil registration. Transgender individuals are likely to be overrepresented in suicides and in victims of homicides. Although there are no precise data on the prevalence of transgender individuals in the group of unidentified deceased, the remarkably high reported prevalence in the general population and the over-representation of transgender individuals in suicides and homicides suggest that the topic is relevant to forensic practice. An autopsy does not always provide evidence of transgender identity, for example in skeletal remains. Particularly in unsolved cases, the possibility that an unidentified person may have been transgender should be considered. Knowledge and awareness of forensic practitioners on this topic should be strengthened by research and training. Databases and data reporting should be optimized. Recording in antemortem databases should clearly distinguish between 'biological sex' and 'apparent sex /lived gender identity'. When collecting postmortem data, a clear distinction should be made between "chromosomal sex" and "sex based on morphological findings". CLINICAL TRIAL NUMBER: Not applicable (review article).

Stillbirth rates following the change in definition of fetal mortality in Quebec.

OBJECTIVES: In 2019, Quebec changed its stillbirth definition to include fetal deaths at 20 weeks gestation or more. Previously, the criterion was a minimum birth weight of 500 g. We assessed the impact of the new definition on stillbirth rates. METHODS: We conducted a retrospective study of stillbirth rates between 2010 and 2021 in Quebec. The exposure consisted of the period during the new definition versus the preceding period. We assessed how the new definition affected stillbirth rates using interrupted time series regression, and compared the period during the new definition with the preceding period using prevalence differences and prevalence ratios with 95% confidence intervals (CI). We determined the extent to which fetuses at the limit of viability (under 500 g or 20‒23 weeks) accounted for any increase in rates. RESULTS: Stillbirth rates went from 4.11 before the new definition to 6.76 per 1000 total births immediately after. Overall, the change in definition led to an absolute increase of 2.58 stillbirths per 1000 total births, for a prevalence ratio of 1.76 (95% CI 1.61‒1.92) compared with the preceding period. Fetal deaths due to congenital anomalies increased by 6.82 per 10,000 (95% CI 4.85‒8.78), while deaths due to pregnancy termination increased by 10.47 per 10,000 (95% CI 8.04‒12.89). Once the definition changed, 37% of stillbirths were under 500 g and 42% were between 20 and 23 weeks, with around half of these caused by congenital anomalies and terminations. CONCLUSION: Stillbirth rates increased after the definition changed in Quebec, mainly due to congenital anomalies and pregnancy terminations.

RéSUMé: OBJECTIFS: En 2019, le Québec a modifié sa définition de mortinaissance pour inclure les morts fœtales à 20 semaines de gestation ou plus. Auparavant, le critère était un poids minimum de 500 g à la naissance. Nous avons évalué l'impact du changement de définition sur la mesure de mortinatalité. MéTHODES: Nous avons mené une étude rétrospective de la mortinatalité entre 2010 et 2021 au Québec. L'exposition était la période après l'introduction de la nouvelle définition par rapport à la période précédente. Nous avons évalué l'impact du changement de définition sur la prévalence de la mortinatalité en utilisant des régressions de séries temporelles interrompues, et en comparant la période suivant le changement de définition avec la période précédente à l'aide de différences de prévalences et de ratios de prévalences avec des intervalles de confiance à 95% (IC). Nous avons déterminé dans quelle mesure les fœtus à la limite de la viabilité (moins de 500 g ou 20 à 23 semaines) contribuaient à l'augmentation. RéSULTATS: La prévalence de la mortinatalité est passé de 4,11 avant la nouvelle définition à 6,76 pour 1 000 naissances immédiatement après le changement de définition. Il y a eu une augmentation absolue de 2,58 mortinaissances pour 1 000 naissances, pour un ratio de prévalences de 1,76 (IC à 95% 1,61‒1,92) comparativement à la période précédente. Les mortinaissances dues aux anomalies congénitales ont augmenté de 6,82 pour 10 000 (IC 95% 4,85‒8,78), tandis que les décès dus aux interruptions de grossesse ont augmenté de 10,47 pour 10 000 (IC 95% 8,04‒12,89). Une fois la définition modifiée, 37 % des mortinaissances survenaient chez des fœtus pesant moins de 500 g et 42 % avaient lieu entre 20 et 23 semaines, la moitié d'entre elles étant dues à des anomalies congénitales et interruptions de grossesse. CONCLUSION: La prévalence de la mortinatalité a augmenté après le changement de définition au Québec, principalement en raison des décès causés par des anomalies congénitales et des interruptions de grossesse.

A review on organ deformation modeling approaches for reliable surgical navigation using augmented reality.

Augmented Reality (AR) holds the potential to revolutionize surgical procedures by allowing surgeons to visualize critical structures within the patient's body. This is achieved through superimposing preoperative organ models onto the actual anatomy. Challenges arise from dynamic deformations of organs during surgery, making preoperative models inadequate for faithfully representing intraoperative anatomy. To enable reliable navigation in augmented surgery, modeling of intraoperative deformation to obtain an accurate alignment of the preoperative organ model with the intraoperative anatomy is indispensable. Despite the existence of various methods proposed to model intraoperative organ deformation, there are still few literature reviews that systematically categorize and summarize these approaches. This review aims to fill this gap by providing a comprehensive and technical-oriented overview of modeling methods for intraoperative organ deformation in augmented reality in surgery. Through a systematic search and screening process, 112 closely relevant papers were included in this review. By presenting the current status of organ deformation modeling methods and their clinical applications, this review seeks to enhance the understanding of organ deformation modeling in AR-guided surgery, and discuss the potential topics for future advancements.