The Urgent Need for Neuroscience Research to Consider Culture when Assessing the Development of Gait in Autistic Children: A Scoping Review.

BACKGROUND: Over the last decade, there has been a steady increase in the number of children diagnosed with autism spectrum disorder (ASD) on a global scale, impacting all racial and cultural groups. This increase in the diagnostic rate has prompted investigation into a myriad of factors that may serve as early signs of ASD. One of these factors includes the biomechanics of gait, or the manner of walking. Although ASD is a spectrum, many autistic children experience differences in gross motor function, including gait. It has been documented that gait is also impacted by racial and cultural background. Given that ASD is equally prevalent across all cultural backgrounds, it is urgent that studies assessing gait in autistic children consider the impact of cultural factors on children's development of gait. The purpose of the present scoping review was to assess whether recent empirical research studies focusing on gait in autistic children have taken culture into account. METHODS: To do so, we conducted a scoping review following PRISMA guidelines using a keyword searching with the terms autism, OR autism spectrum disorder, OR ASD, OR autis, AND gait OR walking in the following databases: CINAHL, ERIC (EBSCO), Medline, ProQuest Nursing & Allied Health Source, PsychInfo, PubMed, and Scopus. Articles were considered for review if they met all six of the following inclusionary criteria: (1) included participants with a diagnosis of autism spectrum disorder (ASD), (2) directly measured gait or walking, (3) the article was a primary study, (4) the article was written in English, (5) participants included children up to age 18, and (6) the article was published between 2014 and 2022. RESULTS: A total of 43 articles met eligibility criteria but none of the articles took culture into account in the data analysis process. CONCLUSIONS: There is an urgent need for neuroscience research to consider cultural factors when assessing gait characteristics of autistic children. This would allow for more culturally responsive and equitable assessment and intervention planning for all autistic children.

A graphical user interface for automated 2- or 3-dimensional image registration in dental treatment recovery planning: the DentIR application.

OBJECTIVES: Cone-beam computed tomography (CBCT) scans enable quantification of interproximal bone loss after implant procedures in dental patients. In order for this quantification to be accurate, software is typically used to manipulate image sets captured before and after implantation to obtain their exact registration (i.e., alignment). However, no affordable CBCT image registration software is currently available for dental applications. Thus, the aim of the present study was to develop a freely available graphical user interface, called DentIR, that automates 2-dimensional (2-D) or 3-D image registration for use in planning dental treatment. METHODS: The DentIR app was designed using the MATLAB environment, downloaded to a desktop personal computer (PC and Mac), and tested for its ease of use and alignment accuracy in the absence of the MATLAB environment. RESULTS: The DentIR app enabled previewing of the CBCT images in 3-D to allow for filtering of each frame to reduce noise and blurring before registration. The 2-D or 3-D registration was tested with four transformation methods. The accuracy of each method was assessed by comparing the mean squared error and the peak signal-to-noise ratio values that were provided by the DentIR app. The registered images could be saved as Portable Network Graphics (PNG) images. CONCLUSIONS: The free, user-friendly DentIR app was easily downloadable to Mac or PC platforms. It provided accurate image registration to aid in the planning of dental treatment. Future updates of the DentIR app include adding the ability to register more than two images at once, enhancing image editing options and enabling registration of a cropped portion of the image for more in-depth analyses.

Automatic vertebra segmentation on dynamic magnetic resonance imaging.

The automatic extraction of the vertebra's shape from dynamic magnetic resonance imaging (MRI) could improve understanding of clinical conditions and their diagnosis. It is hypothesized that the shape of the sacral curve is related to the development of some gynecological conditions such as pelvic organ prolapse (POP). POP is a critical health condition for women and consists of pelvic organs dropping from their normal position. Dynamic MRI is used for assessing POP and to complement clinical examination. Studies have shown some evidence on the association between the shape of the sacral curve and the development of POP. However, the sacral curve is currently extracted manually limiting studies to small datasets and inconclusive evidence. A method composed of an adaptive shortest path algorithm that enhances edge detection and linking, and an improved curve fitting procedure is proposed to automate the identification and segmentation of the sacral curve on MRI. The proposed method uses predetermined pixels surrounding the sacral curve that are found through edge detection to decrease computation time compared to other model-based segmentation algorithms. Moreover, the proposed method is fully automatic and does not require user input or training. Experimental results show that the proposed method can accurately identify sacral curves for nearly 91% of dynamic MRI cases tested in this study. The proposed model is robust and can be used to effectively identify bone structures on MRI.

Automated and simultaneous fovea center localization and macula segmentation using the new dynamic identification and classification of edges model.

Detecting the position of retinal structures, including the fovea center and macula, in retinal images plays a key role in diagnosing eye diseases such as optic nerve hypoplasia, amblyopia, diabetic retinopathy, and macular edema. However, current detection methods are unreliable for infants or certain ethnic populations. Thus, a methodology is proposed here that may be useful for infants and across ethnicities that automatically localizes the fovea center and segments the macula on digital fundus images. First, dark structures and bright artifacts are removed from the input image using preprocessing operations, and the resulting image is transformed to polar space. Second, the fovea center is identified, and the macula region is segmented using the proposed dynamic identification and classification of edges (DICE) model. The performance of the method was evaluated using 1200 fundus images obtained from the relatively large, diverse, and publicly available Messidor database. In 96.1% of these 1200 cases, the distance between the fovea center identified manually by ophthalmologists and automatically using the proposed method remained within 0 to 8 pixels. The dice similarity index comparing the manually obtained results with those of the model for macula segmentation was 96.12% for these 1200 cases. Thus, the proposed method displayed a high degree of accuracy. The methodology using the DICE model is unique and advantageous over previously reported methods because it simultaneously determines the fovea center and segments the macula region without using any structural information, such as optic disc or blood vessel location, and it may prove useful for all populations, including infants.

Automated Localization of Multiple Pelvic Bone Structures on MRI.

In this paper, we present a fully automated localization method for multiple pelvic bone structures on magnetic resonance images (MRI). Pelvic bone structures are at present identified manually on MRI to locate reference points for measurement and evaluation of pelvic organ prolapse (POP). Given that this is a time-consuming and subjective procedure, there is a need to localize pelvic bone structures automatically. However, bone structures are not easily differentiable from soft tissue on MRI as their pixel intensities tend to be very similar. In this paper, we present a model that combines support vector machines and nonlinear regression capturing global and local information to automatically identify the bounding boxes of bone structures on MRI. The model identifies the location of the pelvic bone structures by establishing the association between their relative locations and using local information such as texture features. Results show that the proposed method is able to locate the bone structures of interest accurately (dice similarity index >0.75) in 87-91% of the images. This research aims to enable accurate, consistent, and fully automated localization of bone structures on MRI to facilitate and improve the diagnosis of health conditions such as female POP.

MRI-based segmentation of pubic bone for evaluation of pelvic organ prolapse.

Pelvic organ prolapse (POP) is a major women's health problem. Its diagnosis through magnetic resonance imaging (MRI) has become popular due to current inaccuracies of clinical examination. The diagnosis of POP on MRI consists of identifying reference points on pelvic bone structures for measurement and evaluation. However, it is currently performed manually, making it a time-consuming and subjective procedure. We present a new segmentation approach for automating pelvic bone point identification on MRI. It consists of a multistage mechanism based on texture-based block classification, leak detection, and prior shape information. Texture-based block classification and clustering analysis using K-means algorithm are integrated to generate the initial bone segmentation and to identify leak areas. Prior shape information is incorporated to obtain the final bone segmentation. Then, the reference points are identified using morphological skeleton operation. Results demonstrate that the proposed method achieves higher bone segmentation accuracy compared to other segmentation methods. The proposed method can also automatically identify reference points faster and with more consistency compared with the manually identified point process by experts. This research aims to enable faster and consistent pelvic measurements on MRI to facilitate and improve the diagnosis of female POP.

Fully automated localization of multiple pelvic bone structures on MRI.

In this paper, we present a fully automated localization method for multiple pelvic bone structures on magnetic resonance images (MRI). Pelvic bone structures are currently identified manually on MRI to identify reference points for measurement and evaluation of pelvic organ prolapse (POP). Given that this is a time-consuming and subjective procedure, there is a need to localize pelvic bone structures without any user interaction. However, bone structures are not easily differentiable from soft tissue on MRI as their pixel intensities tend to be very similar. In this research, we present a model that automatically identifies the bounding boxes of the bone structures on MRI using support vector machines (SVM) based classification and non-linear regression model that captures global and local information. Based on the relative locations of pelvic bones and organs, and local information such as texture features, the model identifies the location of the pelvic bone structures by establishing the association between their locations. Results show that the proposed method is able to locate the bone structures of interest accurately. The pubic bone, sacral promontory, and coccyx were correctly detected (DSI > 0.75) in 92%, 90%, and 88% of the testing images. This research aims to enable accurate, consistent and fully automated identification of pelvic bone structures on MRI to facilitate and improve the diagnosis of female pelvic organ prolapse.