New video game developed to help identify attention deficit disorder in children

A team of researchers has developed a platform that allows the identification and evaluation of the degree of attention deficit hyperactivity disorder (ADHD) in children and adolescents. The study was led by researchers from the Universidad Carlos III de Madrid (UC3M) and the Complutense University of Madrid (UCM, in its Spanish acronym), among other institutions.

ADHD is a neurodevelopmental disorder with an estimated prevalence of 7.2 per cent in children and adolescents, according to the latest evaluations.

It is clinically diagnosed on the basis of the judgement of healthcare professionals using the patient’s medical history, often supported by scales completed by caregivers and/or teachers. No diagnostic test has been developed for ADHD to date.

In a paper recently published in Brain Sciences, this team of researchers proposed using a video game that children are already familiar with to identify the symptoms of ADHD and evaluate the severity of the lack of attention in each case.

In this game genre, the player has a running avatar, which they have to use to avoid different obstacles on their way. “In our game, the avatar is a raccoon that has to jump in order to avoid falling into holes it encounters on its route,” explains David Delgado Gomez, the lead author and professor at the UC3M’s Department of Statistics.

“We hypothesise that children diagnosed with ADHD inattentive subtype will make more mistakes by omission and will jump closer to the hole as a result of the symptoms of inattention,” says another author Inmaculada Penuelas Calvo, a psychiatrist at Jimenez Diaz Foundation University Hospital and professor at the UCM’s Department of Personality, Evaluation and Clinical Psychology.

The main benefit of this study is that it allows symptoms of attention deficiency to be directly identified so that the severity of the patient’s inattention can be objectively assessed, say the researchers. Therefore, it could be used to supplement the initial diagnosis as well as to assess the evolution of symptoms or even the effectiveness of treatment.

There are also other important advantages such as the fact that each test would only take seven minutes to complete and does not require specific hardware, which reduces its cost significantly. In fact, conventional personal computers, tablets, or mobile devices can be used, allowing remote assessments to be done.

“Our results indicate that a shorter test may be enough to accurately assess the clinical symptoms of ADHD. This feature makes it particularly attractive in clinical settings where there is a lack of time,” the researchers note.

A rapid test that allows early diagnosis

The study was carried out in collaboration with a group of 32 children, between 8 and 16 years of age, diagnosed with ADHD by the Child and Adolescent Psychiatry Unit at the Jimenez Diaz Foundation University Hospital.

As each child was taking the test, supervised by a trained professional, the appropriate caregiver completed the inattention subscale in the attention deficit hyperactivity disorder and normal behaviour symptom classification scale (SWAN), which is an inventory of reports from parents and caregivers developed to evaluate the ADHD symptoms. In the game, the raccoon has to jump over 180 holes that are grouped into 18 blocks.

“Each block is identified by the speed of the raccoon, the length of the trunk, and the width of the hole. The length of the trunk and the speed of the avatar determine the time between stimuli, which is about 1.5, 2.5, and 3.5 seconds, while the width of the hole determines how difficult it is to jump over,” Penuelas explains.

Currently, ADHD diagnosis depends mainly on the healthcare professionals’ experience and the teacher or caregiver’s observation skills. Several studies have determined that these assessments may be altered, by affective factors for example.

Therefore, “the development of diagnostic methods such as those proposed in this paper may favour early diagnosis and thus improve these patients’ prognosis,” Gomez concludes.

 

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Team Identifies Path to Blood Test for Artery Disorder

A Mount Sinai team has gained valuable insights into fibromuscular dysplasia (FMD), a disorder of the arteries that is typically diagnosed in otherwise healthy women at midlife and sometimes causes aneurysms or serious heart disease. The findings could open the door to new strategies for the diagnosis, treatment, and management of FMD, including a blood test that could lead to earlier diagnosis.

“A lot of work remains, but we have proved that a reliable, blood-based test for this disease is eminently possible,” says Jason C. Kovacic, MD, PhD, Professor of Medicine (Cardiology), Icahn School of Medicine at Mount Sinai, and corresponding author of a study published in August 2019 in Cardiovascular Research. “Such a test could have an enormous impact on the management of FMD. It could pave the way for screening and counseling of family members, and for tailoring clinical care to patients who, in many cases, remain undiagnosed until they suffer a major event.”

The team compared blood samples from 90 women with FMD and 100 women without it, evaluating nearly 1,000 proteins and 31 lipid subclasses. Eventually, researchers identified and validated 37 proteins and 10 lipid sub-classes that make up a unique FMD disease signature. Then, machine learning was used to develop a prototype blood test for FMD.

This is preliminary, but it is the first meaningful, mechanistic research that has been done in this disease,” says Jeffrey W. Olin, DO, Professor of Medicine (Cardiology), Icahn School of Medicine, and a co-author of the study. Developing an accurate test is crucial, says Dr. Olin, a leader in the treatment of FMD. “It’s not uncommon for a patient to have high blood pressure related to FMD that started when she was 30, and not have FMD diagnosed until she is 50,” he says.

FMD is a genetic disease that predominantly affects women and can strike at any age, although the average age of patients at diagnosis is 52. Abnormal cells form in the arteries, which take on a characteristic “string of beads” appearance, causing narrowing, tearing, or bulging of the vessels.

“FMD can affect the arteries of the kidney, causing high blood pressure. It can affect the arteries to the brain, which can cause stroke, or it can affect the arteries in the heart, in which you can develop a heart attack,” Dr. Olin explains. The prevalence of the disease is hard to gauge, because most patients with FMD have no symptoms for many years, and it is often found in a scan for another clinical purpose. For example, “FMD is discovered in approximately 4 percent of potential kidney donors, but this may be a serious underestimation of its prevalence,” he says.

Mount Sinai is in the forefront of efforts to unravel the genetics of FMD, due to the work of Dr. Kovacic and Dr. Olin, who is principal investigator for the United States Registry for Fibromuscular Dysplasia, and Director of the Mount Sinai Heart Center for Fibromuscular Dysplasia Care and Research. The team also includes Daniella Kadian-Dodov, MD, Assistant Professor of Medicine (Cardiology); Valentina d’Escamard, PhD, a senior scientist in the Kovacic Lab; and Annette King, NP, study coordinator.

“We are looking for a genetic profile of this disease, and we do have some promising preliminary results,” Dr. Olin says. “But ultimately, we want to find the gene or genes that cause this disease, and develop a treatment that blocks the effects of those genes.”

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New AI software can identify types of breast cancers

Scientists are developing a new way to identify the unique chemical 'fingerprints' for different types of breast cancers. These new chemical footprints will be used to train AI (artificial intelligence) software - creating a new tool for rapid and accurate diagnosis of breast cancers.

The team of researchers from Lancaster University and Airedale NHS Foundation Trust are using a specialised chemical analytical technique called Raman Spectroscopy on biopsies to identify the molecular structure of different types of breast cancer, as well as variations within each cancer cell group.

The results of the study were published in the journal Expert Review of Molecular Diagnostics.

Raman analysis is able to provide real-time information on cells and can be used to check how the cells are behaving, spreading and emerging elsewhere in the body.

After identifying the chemical fingerprints of breast cancer cells, and observing how they change, the researchers used this information to train complex machine learning algorithms to identify four subtypes of cancer.

The algorithms successfully predicted diagnostic patterns for each subtype with a high level of accuracy ranging between 70 per cent and 100 per cent.

Similar versions of these algorithms have previously been used to identify other forms of caners and diseases such as skin, oral and lung cancers.

The next stage of the research will look at creating databases of the chemical structures of many more different types of breast cancer cells and the forms they can take.

These databases will be then used to train more artificial intelligent algorithms using machine learning - eventually leading to a new diagnostic tool to sit alongside mammograms and MRI scans.

The new algorithms promise to provide rapid information to help medical specialists to make quicker diagnosis.

In addition, the approach will help to determine the state of the disease at various points in its progression and will become critical in planning the therapeutic approach of individual patients.

Professor Ihtesham Rehman, Chair in Bioengineering at Lancaster University and senior author of the study, said: "This research is an important step in developing a new way to identify the chemical structures of different types of breast cancers. We have been able to use these 'fingerprints' to develop complex algorithms that are accurately able to identify cells of four different types of cancer types.

"Vibrational spectroscopy combined with data mining and machine learning has the potential to offer a real-time analysis in biological samples, including cancer, with excellent accuracy - creating a powerful new tool to sit alongside existing techniques and helping medical specialists deliver accurate and timely diagnosis for their patients, and for monitoring the progression of the disease."

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