Collaborating Authors


AI Approved for Adults May Aid Interpretation of Pediatric Chest Images – Health IT Analytics


Artificial intelligence-based software approved to interpret adult chest … with Artificial Intelligence Use in Pediatric Care · Machine-Learning …

A pediatric wrist trauma X-ray dataset (GRAZPEDWRI-DX) for machine learning - Scientific Data


Digital radiography is widely available and the standard modality in trauma imaging, often enabling to diagnose pediatric wrist fractures. However, image interpretation requires time-consuming specialized training. Due to astonishing progress in computer vision algorithms, automated fracture detection has become a topic of research interest. This paper presents the GRAZPEDWRI-DX dataset containing annotated pediatric trauma wrist radiographs of 6,091 patients, treated at the Department for Pediatric Surgery of the University Hospital Graz between 2008 and 2018. A total number of 10,643 studies (20,327 images) are made available, typically covering posteroanterior and lateral projections. The dataset is annotated with 74,459 image tags and features 67,771 labeled objects. We de-identified all radiographs and converted the DICOM pixel data to 16-Bit grayscale PNG images. The filenames and the accompanying text files provide basic patient information (age, sex). Several pediatric radiologists annotated dataset images by placing lines, bounding boxes, or polygons to mark pathologies like fractures or periosteal reactions. They also tagged general image characteristics. This dataset is publicly available to encourage computer vision research.

Unbound Medicine Integrates Machine Learning Into Digital Platform - AI Summary


To enhance clinical decision support capabilities for professional societies and healthcare institutions, Unbound developed Unbound Intelligence (UBI)‒exclusive artificial intelligence and machine learning tools to help clinicians keep up to date with current research, as well as discover and fill knowledge gaps. The first medical association to adopt Unbound Intelligence was the American Pediatric Surgical Association (APSA). In 2017 APSA selected Unbound's end-to-end digital publishing platform to develop and power its marquee digital resource, the APSA Pediatric Surgery Library (PSL). "We believe Unbound Intelligence can help transform medicine by delivering new, assistive technologies that empower healthcare providers to better serve their patients," says Bill Detmer, MD, CEO of Unbound Medicine, "We are delighted to partner with the visionary leadership at APSA to power a new era of clinical decision making." To learn more about how Unbound Intelligence can help your organization, contact Unbound Medicine.

How machine learning is helping patients diagnosed with the most common childhood cancer


New software developed by Peter Mac and collaborators is helping patients diagnosed with acute lymphoblastic leukemia (ALL) to determine what subtype they have. ALL is the most common childhood cancer in the world, and also affects adults. "Thirty to forty percent of all childhood cancers are ALL, it's a major pediatric cancer problem," says Associate Professor Paul Ekert from Peter Mac and the Children's Cancer Institute, who was involved in this work. More than 300 people are diagnosed with the disease in Australia each year, and more than half of those are young children under the age of 15. Determining what subtype of ALL a patient has provides valuable information about their prognosis, and how they should best be treated.

A revolutionary bionic exoskeleton helps children walk again


Marsi Bionics, a Spanish tech company, has developed the first ever bionic exoskeleton designed exclusively for children. The revolutionary creation is called ATLAS 2030 and offers full bodily support from feet to torso, with an optional head restraint system. With the help of machine learning algorithms, the exoskeleton is able to decipher a subject's intention to move and immediately translate that into action.

How AI Is Using Facial Detection To Spot Rare Diseases In Children


Andrew was playing under the summer sun in the backyard. As the four-year-old's parents watched, they noticed something seemed off. Perhaps it was his unusually small head or the after-effects of the surgery to correct his congenital disorder. When Andrew's parents consulted Dr. Karen Gripp, Professor of Pediatrics at Nemours Children's Hospital, she decided to investigate. In addition to conventional procedures, she ran a quick diagnosis on Face2Gene, a computer vision-powered app that looks for indications of rare diseases.

Pediatric Refractory Nephrotic Syndrome


we aimed to employ machine learning algorithms to investigate the impact of clinical and genetic variables on the TAC dose/weight-adjusted trough …

Digital Twin Technology: The Future of Predicting Neurological Complications of Pediatric Cancers and Their Treatment


Healthcare technologies have seen a surge in utilization during the COVID 19 pandemic. Remote patient care, virtual follow-up and other forms of futurism will likely see further adaptation both as a preparational strategy for future pandemics and due to the inevitable evolution of artificial intelligence. This manuscript theorizes the healthcare applications of digital twin technology. Digital twin is a triune concept that involves a physical model, a virtual counterpart, and the interplay between the two constructs. This interface between computer science and medicine is a new frontier with broad potential applications. We propose that digital twin technology can exhaustively and methodologically analyze the associations between a physical cancer patient and a corresponding digital counterpart with the goal of isolating predictors of neurological sequalae of disease. This proposition stems from the premise that data science can complement clinical acumen to scientifically inform the diagnostics, treatment planning and prognostication of cancer care. Specifically, digital twin could predict neurological complications through its utilization in precision medicine, modelling cancer care and treatment, predictive analytics and machine learning, and in consolidating various spectra of clinician opinions.

Decision-Focused Learning in Restless Multi-Armed Bandits with Application to Maternal and Child Care Domain Artificial Intelligence

This paper studies restless multi-armed bandit (RMAB) problems with unknown arm transition dynamics but with known correlated arm features. The goal is to learn a model to predict transition dynamics given features, where the Whittle index policy solves the RMAB problems using predicted transitions. However, prior works often learn the model by maximizing the predictive accuracy instead of final RMAB solution quality, causing a mismatch between training and evaluation objectives. To address this shortcoming we propose a novel approach for decision-focused learning in RMAB that directly trains the predictive model to maximize the Whittle index solution quality. We present three key contributions: (i) we establish the differentiability of the Whittle index policy to support decision-focused learning; (ii) we significantly improve the scalability of previous decision-focused learning approaches in sequential problems; (iii) we apply our algorithm to the service call scheduling problem on a real-world maternal and child health domain. Our algorithm is the first for decision-focused learning in RMAB that scales to large-scale real-world problems. \end{abstract}

Our children are growing up with AI: what you need to know


A 2019 study conducted by DataChildFutures found that 46% of participating Italian households had AI-powered speakers, while 40% of toys were connected to the internet. More recent research suggests that by 2023 more than 275 million intelligent voice assistants, such as Amazon Echo or Google Home, will be installed in homes worldwide. As younger generations grow up interacting with AI-enabled devices, more consideration should be given to the impact of this technology on children, their rights and wellbeing. AI-powered learning tools and approaches are often regarded as critical drivers of innovation in the education sector. Often recognized for its ability to improve the quality of learning and teaching, artificial intelligence is being used to monitor students' level of knowledge and learning habits, such as rereading and task prioritization, and ultimately to provide a personalized approach to learning. Knewton is one example of AI-enabled learning software that identifies knowledge gaps and curates education content in line with user needs.