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New AI model may help screen for heart defect from birth

The model can screen electrocardiogram (ECG) for signs of atrial septal defects (ASD), a condition that can cause heart failure and is under-reported due to a lack of symptoms before irreversible complications arise.

A team of international scientists have developed a deep learning artificial intelligence model to screen for heart defect from birth.
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Published : Aug 18, 2023, 9:18 AM IST

New York: A team of international scientists have developed a deep learning artificial intelligence model to screen for heart defect from birth.

The model can screen electrocardiogram (ECG) for signs of atrial septal defects (ASD), a condition that can cause heart failure and is under-reported due to a lack of symptoms before irreversible complications arise. While ECG, takes only about a minute to detect, when humans analyse an ECG readout for known abnormalities associated with ASD, there is limited sensitivity for picking up ASD.

In the study, published in the journal eClinicalMedicine, the team fed a deep learning model ECG data from 80,947 patients in the US and Japan, over 18 who underwent both ECG and echocardiogram to detect ASD. The model was more sensitive than using known abnormalities found on ECGs to screen for ASD. The model correctly detected ASD 93.7 per cent of the time, while using known abnormalities found ASD 80.6 per cent of the time.

"It picked up much more than what an expert does using known abnormalities to identify cases of ASD," said Shinichi Goto, corresponding author and instructor in the Division of Cardiovascular Medicine at Brigham and Women's Hospital. "If we can deploy our model on a population-level ECG screening, we would be able to pick up many more of these patients before they have irreversible damage," Goto added.

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ASD is a common congenital heart disease. It is caused by a hole in the heart's septum that lets blood flow between the left and right atriums. It's diagnosed in about 0.1 per cent to 0.2 per cent of the population but is likely underreported, Goto said. The symptoms of ASD are typically very mild, or in many cases, nonexistent until later in life. Symptoms include an inability to do strenuous exercise, affect the rate or rhythm of the heartbeat, heart palpitations, and an increased risk of pneumonia.

Even if ASD isn't causing symptoms, it can stress the heart and increase the risk of atrial fibrillation, stroke, heart failure, and pulmonary hypertension. At that point, the complications of ASD are irreversible, even if the defect is fixed later. If found early, ASD can be corrected with minimally invasive surgery to improve life expectancy and reduce complications.

The study results suggest that the technology could be used in population-level screening to detect ASD before it leads to irreversible heart damage. ECG is relatively low cost and currently performed in many contexts. "Perhaps this screening could be integrated into an annual PCP appointment or used to screen ECGs taken for other reasons," Goto said. (IANS)

Also read: Seven home remedies to prevent dehydration; how to manage dehydration

New York: A team of international scientists have developed a deep learning artificial intelligence model to screen for heart defect from birth.

The model can screen electrocardiogram (ECG) for signs of atrial septal defects (ASD), a condition that can cause heart failure and is under-reported due to a lack of symptoms before irreversible complications arise. While ECG, takes only about a minute to detect, when humans analyse an ECG readout for known abnormalities associated with ASD, there is limited sensitivity for picking up ASD.

In the study, published in the journal eClinicalMedicine, the team fed a deep learning model ECG data from 80,947 patients in the US and Japan, over 18 who underwent both ECG and echocardiogram to detect ASD. The model was more sensitive than using known abnormalities found on ECGs to screen for ASD. The model correctly detected ASD 93.7 per cent of the time, while using known abnormalities found ASD 80.6 per cent of the time.

"It picked up much more than what an expert does using known abnormalities to identify cases of ASD," said Shinichi Goto, corresponding author and instructor in the Division of Cardiovascular Medicine at Brigham and Women's Hospital. "If we can deploy our model on a population-level ECG screening, we would be able to pick up many more of these patients before they have irreversible damage," Goto added.

Also read: Doctors remove over 15 kg cyst from woman that made her look pregnant

ASD is a common congenital heart disease. It is caused by a hole in the heart's septum that lets blood flow between the left and right atriums. It's diagnosed in about 0.1 per cent to 0.2 per cent of the population but is likely underreported, Goto said. The symptoms of ASD are typically very mild, or in many cases, nonexistent until later in life. Symptoms include an inability to do strenuous exercise, affect the rate or rhythm of the heartbeat, heart palpitations, and an increased risk of pneumonia.

Even if ASD isn't causing symptoms, it can stress the heart and increase the risk of atrial fibrillation, stroke, heart failure, and pulmonary hypertension. At that point, the complications of ASD are irreversible, even if the defect is fixed later. If found early, ASD can be corrected with minimally invasive surgery to improve life expectancy and reduce complications.

The study results suggest that the technology could be used in population-level screening to detect ASD before it leads to irreversible heart damage. ECG is relatively low cost and currently performed in many contexts. "Perhaps this screening could be integrated into an annual PCP appointment or used to screen ECGs taken for other reasons," Goto said. (IANS)

Also read: Seven home remedies to prevent dehydration; how to manage dehydration

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