Medicine and Health Sciences Commons^™

Co-Designing Situated Displays For Family Co-Regulation With Adhd Children, Lucas M. Silva, Franceli L. Cibrian, Clarisse Bonang, Arpita Bhattacharya, Aehong Min, Elissa M. Monteiro, Jesus A. Beltran, Sabrina E. B. Schuck, Kimberley D. Lakes, Gillian R. Hayes, Daniel A. Epstein

Engineering Faculty Articles and Research

Family informatics often uses shared data dashboards to promote awareness of each other’s health-related behaviors. However, these interfaces often stop short of providing families with needed guidance around how to improve family functioning and health behaviors. We consider the needs of family co-regulation with ADHD children to understand how in-home displays can support family well-being. We conducted three co-design sessions with each of eight families with ADHD children who had used a smartwatch for self-tracking. Results indicate that situated displays could nudge families to jointly use their data for learning and skill-building. Accommodating individual needs and preferences when family members …

Advancing Brain Tumor Segmentation With Spectral–Spatial Graph Neural Networks, Sina Mohammadi, Mohamed Allali

Engineering Faculty Articles and Research

In the field of brain tumor segmentation, accurately capturing the complexities of tumor sub-regions poses significant challenges. Traditional segmentation methods usually fail to accurately segment tumor subregions. This research introduces a novel solution employing Graph Neural Networks (GNNs), enriched with spectral and spatial insight. In the supervoxel creation phase, we explored methods like VCCS, SLIC, Watershed, Meanshift, and Felzenszwalb–Huttenlocher, evaluating their performance based on homogeneity, moment of inertia, and uniformity in shape and size. After creating supervoxels, we represented 3D MRI images as a graph structure. In this study, we combined Spatial and Spectral GNNs to capture both local and …

Investigating Interactive Methods In Remote Chestfeeding Support For Lactation Consulting Professionals In Brazil, Jessica De Souza, Cinthia Calsinski, Kristina Chamberlain, Franceli L. Cibrian, Edward Jay Wang

Engineering Faculty Articles and Research

Objective: Lactation consultants (LCs) positively impact chestfeeding rates by providing in-person support to struggling parents. In Brazil, LCs are a scarce resource and in high demand, risking chestfeeding rates across many communities nationwide. The transition to remote consultations during the COVID-19 pandemic made LCs face several challenges to solve chestfeeding problems due to limited technical resources for management, communication, and diagnosis. This study investigates the main technological issues LCs have in remote consultations and what technology features are helpful for chestfeeding problem-solving in remote settings.

Methods: This paper implements qualitative investigation through a contextual study (n = 10) and …

Split And Join: An Efficient Approach For Simulating Stapled Intestinal Anastomosis In Virtual Reality, Di Qi, Suvranu De

Engineering Faculty Articles and Research

Colorectal cancer is a life-threatening disease. It is the second leading cause of cancer-related deaths in the United States. Stapled anastomosis is a rapid treatment for colorectal cancer and other intestinal diseases and has become an integral part of routine surgical practice. However, to the best of our knowledge, there is no existing work simulating intestinal anastomosis that often involves sophisticated soft tissue manipulations such as cutting and stitching. In this paper, for the first time, we propose a novel split and join approach to simulate a side-to-side stapled intestinal anastomosis in virtual reality. We mimic the intestine model using …

Time-To-Event Modeling For Hospital Length Of Stay Prediction For Covid-19 Patients, Yuxin Wen, Md. Fashiar Rahman, Yan Zhuang, Michael Pokojovy, Honglun Xu, Peter Mccaffrey, Alexander Vo, Eric Walser, Scott Moen, Tzu-Liang Bill Tseng

Engineering Faculty Articles and Research

Providing timely patient care while maintaining optimal resource utilization is one of the central operational challenges hospitals have been facing throughout the pandemic. Hospital length of stay (LOS) is an important indicator of hospital efficiency, quality of patient care, and operational resilience. Numerous researchers have developed regression or classification models to predict LOS. However, conventional models suffer from the lack of capability to make use of typically censored clinical data. We propose to use time-to-event modeling techniques, also known as survival analysis, to predict the LOS for patients based on individualized information collected from multiple sources. The performance of six …

When Worlds Collide: Boundary Management Of Adolescent And Young Adult Childhood Cancer Survivors And Caregivers, Elizabeth A. Ankrah, Arpita Bhattacharya, Lissamarie Donjuan, Franceli L. Cibrian, Anamara Ritt-Olson, Joel Milam, Lilibeth Torno, Gillian R. Hayes

Engineering Faculty Articles and Research

Adolescent and young adult childhood cancer survivors experience health complications, late or long-term biomedical complications, as well as economic and psychosocial challenges that can have a lifelong impact on their quality-of-life. As childhood cancer survivors transition into adulthood, they must learn to balance their identity development with demands of everyday life and the near- and long-term consequences of their cancer experience, all of which have implications for the ways they use existing technologies and the design of novel technologies. In this study, we interviewed 24 childhood cancer survivors and six caregivers about their cancer survivorship experiences. The results of our …

Machine Learning Based Medical Image Deepfake Detection: A Comparative Study, Siddharth Solaiyappan, Yuxin Wen

Engineering Faculty Articles and Research

Deep generative networks in recent years have reinforced the need for caution while consuming various modalities of digital information. One avenue of deepfake creation is aligned with injection and removal of tumors from medical scans. Failure to detect medical deepfakes can lead to large setbacks on hospital resources or even loss of life. This paper attempts to address the detection of such attacks with a structured case study. Specifically, we evaluate eight different machine learning algorithms, which include three conventional machine learning methods (Support Vector Machine, Random Forest, Decision Tree) and five deep learning models (DenseNet121, DenseNet201, ResNet50, ResNet101, VGG19) …

Applications Of Unsupervised Machine Learning In Autism Spectrum Disorder Research: A Review, Chelsea Parlett-Pelleriti, Elizabeth Stevens, Dennis R. Dixon, Erik J. Linstead

Engineering Faculty Articles and Research

Large amounts of autism spectrum disorder (ASD) data is created through hospitals, therapy centers, and mobile applications; however, much of this rich data does not have pre-existing classes or labels. Large amounts of data—both genetic and behavioral—that are collected as part of scientific studies or a part of treatment can provide a deeper, more nuanced insight into both diagnosis and treatment of ASD. This paper reviews 43 papers using unsupervised machine learning in ASD, including k-means clustering, hierarchical clustering, model-based clustering, and self-organizing maps. The aim of this review is to provide a survey of the current uses of …

Feel And Touch: A Haptic Mobile Game To Assess Tactile Processing, Ivonne Monarca, Monica Tentori, Franceli L. Cibrian

Engineering Faculty Articles and Research

Haptic interfaces have great potential for assessing the tactile processing of children with Autism Spectrum Disorder (ASD), an area that has been under-explored due to the lack of tools to assess it. Until now, haptic interfaces for children have mostly been used as a teaching or therapeutic tool, so there are still open questions about how they could be used to assess tactile processing of children with ASD. This article presents the design process that led to the development of Feel and Touch, a mobile game augmented with vibrotactile stimuli to assess tactile processing. Our feasibility evaluation, with 5 children …

Digital Markers Of Autism, Ivonne Monarca, Franceli L. Cibrian, Monica Tentori

Engineering Faculty Articles and Research

Autism Spectrum Disorder (ASD) is a neurological condition that affects how a people communicate and interact with others. The use of screening tools during childhood is very important to detect those children who need to be referred for a diagnosis of ASD. However, most screening tools are based on parents' responses so the result can be subjective. In addition, most screening tools focus on social and communicative skills leaving aside sensory features, which have shown to have the potential to be ASD markers. Tactile processing has been little explored due to lack of tools to asses it, however with the …

Unsupervised Machine Learning For Identifying Challenging Behavior Profiles To Explore Cluster-Based Treatment Efficacy In Children With Autism Spectrum Disorder: Retrospective Data Analysis Study, Julie Gardner-Hoag, Marlena N. Novack, Chelsea Parlett-Pelleriti, Elizabeth Stevens, Dennis Dixon, Erik Linstead

Engineering Faculty Articles and Research

Background: Challenging behaviors are prevalent among individuals with autism spectrum disorder; however, research exploring the impact of challenging behaviors on treatment response is lacking.

Objective: The purpose of this study was to identify types of autism spectrum disorder based on engagement in different challenging behaviors and evaluate differences in treatment response between groups.

Methods: Retrospective data on challenging behaviors and treatment progress for 854 children with autism spectrum disorder were analyzed. Participants were clustered based on 8 observed challenging behaviors using k means, and multiple linear regression was performed to test interactions between skill mastery and treatment …

Interactive Sonification To Assist Children With Autism During Motor Therapeutic Interventions, Franceli L. Cibrian, Judith Ley-Flores, Joseph W. Newbold, Aneesha Singh, Nadia Bianchi-Berthouze, Monica Tentori

Engineering Faculty Articles and Research

Interactive sonification is an effective tool used to guide individuals when practicing movements. Little research has shown the use of interactive sonification in supporting motor therapeutic interventions for children with autism who exhibit motor impairments. The goal of this research is to study if children with autism understand the use of interactive sonification during motor therapeutic interventions, its potential impact of interactive sonification in the development of motor skills in children with autism, and the feasibility of using it in specialized schools for children with autism. We conducted two deployment studies in Mexico using Go-with-the-Flow, a framework to sonify movements …

Supporting Coordination Of Children With Asd Using Neurological Music Therapy: A Pilot Randomized Control Trial Comparing An Elastic Touch-Display With Tambourines, Franceli L. Cibrian, Melisa Madrigal, Marina Avelais, Monica Tentori

Engineering Faculty Articles and Research

Aim

To evaluate the efficacy of Neurologic Music Therapy (NMT) using a traditional and a technological intervention (elastic touch-display) in improving the coordination of children with Autism Spectrum Disorder (ASD), as a primary outcome, and the timing and strength control of their movements as secondary outcomes.

Methods

Twenty-two children with ASD completed 8 NMT sessions, as a part of a 2-month intervention. Participants were randomly assigned to either use an elastic touch-display (experimental group) or tambourines (control group). We conducted pre- and post- assessment evaluations, including the Developmental Coordination Disorder Questionnaire (DCDQ) and motor assessments related to the control of …

Exploring The Eating Disorder Examination Questionnaire, Clinical Impairment Assessment, And Autism Quotient To Identify Eating Disorder Vulnerability: A Cluster Analysis, Natalia Stewart Rosenfield, Erik Linstead

Engineering Faculty Articles and Research

Eating disorders are very complicated and many factors play a role in their manifestation. Furthermore, due to the variability in diagnosis and symptoms, treatment for an eating disorder is unique to the individual. As a result, there are numerous assessment tools available, which range from brief survey questionnaires to in-depth interviews conducted by a professional. One of the many benefits to using machine learning is that it offers new insight into datasets that researchers may not previously have, particularly when compared to traditional statistical methods. The aim of this paper was to employ k-means clustering to explore the Eating Disorder …

Circus In Motion: A Multimodal Exergame Supporting Vestibular Therapy For Children With Autism, Oscar Peña, Franceli L. Cibrian, Monica Tentori

Engineering Faculty Articles and Research

Exergames are serious games that involve physical exertion and are thought of as a form of exercise by using novel input models. Exergames are promising in improving the vestibular differences of children with autism but often lack of adaptation mechanisms that adjust the difficulty level of the exergame. In this paper, we present the design and development of Circus in Motion, a multimodal exergame supporting children with autism with the practice of non-locomotor movements. We describe how the data from a 3D depth camera enables the tracking of non-locomotor movements allowing children to naturally interact with the exergame . A …

Admittance Method For Estimating Local Field Potentials Generated In A Multi-Scale Neuron Model Of The Hippocampus, Clayton S. Bingham, Javad Paknahad, Christopher Bc Girard, Kyle Loizos, Jean-Marie C. Bouteiller, Dong Song, Gianluca Lazzi, Theodore W. Berger

Engineering Faculty Articles and Research

Significant progress has been made toward model-based prediction of neral tissue activation in response to extracellular electrical stimulation, but challenges remain in the accurate and efficient estimation of distributed local field potentials (LFP). Analytical methods of estimating electric fields are a first-order approximation that may be suitable for model validation, but they are computationally expensive and cannot accurately capture boundary conditions in heterogeneous tissue. While there are many appropriate numerical methods of solving electric fields in neural tissue models, there isn't an established standard for mesh geometry nor a well-known rule for handling any mismatch in spatial resolution. Moreover, the …

Ml-Medic: A Preliminary Study Of An Interactive Visual Analysis Tool Facilitating Clinical Applications Of Machine Learning For Precision Medicine, Laura Stevens, David Kao, Jennifer Hall, Carsten Görg, Kaitlyn Abdo, Erik Linstead

Engineering Faculty Articles and Research

Accessible interactive tools that integrate machine learning methods with clinical research and reduce the programming experience required are needed to move science forward. Here, we present Machine Learning for Medical Exploration and Data-Inspired Care (ML-MEDIC), a point-and-click, interactive tool with a visual interface for facilitating machine learning and statistical analyses in clinical research. We deployed ML-MEDIC in the American Heart Association (AHA) Precision Medicine Platform to provide secure internet access and facilitate collaboration. ML-MEDIC’s efficacy for facilitating the adoption of machine learning was evaluated through two case studies in collaboration with clinical domain experts. A domain expert review was also …

The Natural Historian’S Guide To The Ct Galaxy: Step-By-Step Instructions For Preparing And Analyzing Computed Tomographic (Ct) Data Using Cross-Platform, Open Access Software, T. J. Buser, O. F. Boyd, A. Cortés, Cassandra M. Donatelli, M. A. Kolmann, J. L. Luparell, J. A. Pfeiffenberger, B. L. Sidlauskas, A. P. Summers

Engineering Faculty Articles and Research

The decreasing cost of acquiring computed tomographic (CT) data has fueled a global effort to digitize the anatomy of museum specimens. This effort has produced a wealth of open access digital three-dimensional (3D) models of anatomy available to anyone with access to the Internet. The potential applications of these data are broad, ranging from 3D printing for purely educational purposes to the development of highly advanced biomechanical models of anatomical structures. However, while virtually anyone can access these digital data, relatively few have the training to easily derive a desirable product (e.g., a 3D visualization of an anatomical structure) from …

Supporting Self-Regulation Of Children With Adhd Using Wearables: Tensions And Design Challenges, Franceli L. Cibrian, Kimberley D. Lakes, Arya Tavakoulnia, Kayla Guzman, Sabrina Schuck, Gillian R. Hayes

Engineering Faculty Articles and Research

The design of wearable applications supporting children with Attention Deficit Hyperactivity Disorders (ADHD) requires a deep understanding not only of what is possible from a clinical standpoint but also how the children might understand and orient towards wearable technologies, such as a smartwatch. Through a series of participatory design workshops with children with ADHD and their caregivers, we identified tensions and challenges in designing wearable applications supporting the self-regulation of children with ADHD. In this paper, we describe the specific challenges of smartwatches for this population, the balance between self-regulation and co-regulation, and tensions when receiving notifications on a smartwatch …

Learning In The Machine: To Share Or Not To Share?, Jordan Ott, Erik Linstead, Nicholas Lahaye, Pierre Baldi

Engineering Faculty Articles and Research

Weight-sharing is one of the pillars behind Convolutional Neural Networks and their successes. However, in physical neural systems such as the brain, weight-sharing is implausible. This discrepancy raises the fundamental question of whether weight-sharing is necessary. If so, to which degree of precision? If not, what are the alternatives? The goal of this study is to investigate these questions, primarily through simulations where the weight-sharing assumption is relaxed. Taking inspiration from neural circuitry, we explore the use of Free Convolutional Networks and neurons with variable connection patterns. Using Free Convolutional Networks, we show that while weight-sharing is a pragmatic optimization …

Using Green Emitting Ph-Responsive Nanogels To Report Environmental Changes Within Hydrogels: A Nanoprobe For Versatile Sensing, Mingning Zhu, Dongdong Lu, Shanglin Wu, Qing Lian, Wenkai Wang, L. Andrew Lyon, Weiguang Wang, Paulo Bártolo, Brian R. Saunders

Engineering Faculty Articles and Research

Remotely reporting the local environment within hydrogels using inexpensive laboratory techniques has excellent potential to improve our understanding of the nanometer-scale changes that cause macroscopic swelling or deswelling. Whilst photoluminescence (PL) spectroscopy is a popular method for such studies this approach commonly requires bespoke and time-consuming synthesis to attach fluorophores which may leave toxic residues. A promising and more versatile alternative is to use a pre-formed nanogel probe that contains a donor/acceptor pair and then “dope” that into the gel during gel assembly. Here, we introduce green-emitting methacrylic acid-based nanogel probe particles and use them to report the local environment …

A Virtual Reality System For Practicing Conversation Skills For Children With Autism, Natalia Stewart Rosenfield, Kathleen Lamkin, Jennifer Re, Kendra Day, Louanne E. Boyd, Erik J. Linstead

Engineering Faculty Articles and Research

We describe a virtual reality environment, Bob’s Fish Shop, which provides a system where users diagnosed with Autism Spectrum Disorder (ASD) can practice social interactions in a safe and controlled environment. A case study is presented which suggests such an environment can provide the opportunity for users to build the skills necessary to carry out a conversation without the fear of negative social consequences present in the physical world. Through the repetition and analysis of these virtual interactions, users can improve social and conversational understanding.

Applications Of Supervised Machine Learning In Autism Spectrum Disorder Research: A Review, Kayleigh K. Hyde, Marlena N. Novack, Nicholas Lahaye, Chelsea Parlett-Pelleriti, Raymond Anden, Dennis R. Dixon, Erik Linstead

Engineering Faculty Articles and Research

Autism spectrum disorder (ASD) research has yet to leverage "big data" on the same scale as other fields; however, advancements in easy, affordable data collection and analysis may soon make this a reality. Indeed, there has been a notable increase in research literature evaluating the effectiveness of machine learning for diagnosing ASD, exploring its genetic underpinnings, and designing effective interventions. This paper provides a comprehensive review of 45 papers utilizing supervised machine learning in ASD, including algorithms for classification and text analysis. The goal of the paper is to identify and describe supervised machine learning trends in ASD literature as …

Bendablesound: An Elastic Multisensory Surface Using Touch-Based Interactions To Assist Children With Severe Autism During Music Therapy, Franceli L. Cibrian, Oscar Peña, Deysi Ortega, Monica Tentori

Engineering Faculty Articles and Research

Neurological Music Therapy uses live music to improve the sensorimotor regulation of children with severe autism. However, they often lack musical training and their impairments limit their interactions with musical instruments. In this paper, we present our co-design work that led to the BendableSound prototype: an elastic multisensory surface encouraging users to practice coordination movements when touching a fabric to play sounds. We present the results of a formative study conducted with 18 teachers showing BendableSound was perceived as “usable” and “attractive”. Then, we present a deployment study with 24 children with severe autism showing BendableSound is “easy to use” …

Social Compass Curriculum: Three Descriptive Case Studies Of Social Skills Outcomes For Students With Autism, Louanne E. Boyd, Deborah M. Ward

Engineering Faculty Articles and Research

The Social Compass Curriculum (SCC) was investigated for its effectiveness in improving core social skills in three descriptive case studies of students with autism. Treatment fidelity of the SCC was also measured in the school setting. The Social Responsiveness Scale and the Autism Social Skills Profile were completed by parents to measure pre- and postintervention social skills for three students aged 8 to 11 years who participated in the present multisite pilot study. Fidelity of implementation data were collected via a checklist during observations for three educators who implemented the intervention. Results indicate that the SCC improved core social deficits …