Neural Network Outperforms Physicians at Predicting Cardiac Arrest Risk


A study published by researchers from Johns Hopkins University highlights new artificial intelligence tools that could help physicians preemptively identify cardiac arrest in patients with the use of artificial intelligence. This new technology could change the way healthcare professionals approach preventative cardiac care, potentially saving patients from fatal outcomes.


Cardiac arrest is one of the leading causes of death, causing hundreds of thousands of deaths per year in the United States. It is caused by a sudden, often arbitrary failure of the heart and can be attributed to factors such as genetics, diet, arrythmia (abnormal heartbeat), and underlying heart problems. They can be the result of chronic cardiovascular conditions but can also occur in healthy individuals. Despite the vast research regarding the disease, scientists are still not completely sure how cardiac arrest arises in patients. Cardiac arrest is nearly impossible to predict with accuracy, making it one of medicine’s deadliest and most confusing conditions.

Currently, physicians determine a patient’s likelihood of cardiac arrest by analyzing their vitals and heart scarring. Testing of vitals entails quantitative analysis of a patient’s blood, including but not limited to cholesterol and sugar levels. Heart scars are tiny marks in the heart which cause cardiovascular disease, and ultimately, cardiac arrest. However, heart scars are incredibly hard to detect because they are microscopic in size. The team of researchers from Johns Hopkins University sought to develop a solution that could accurately predict cardiac arrest risk.

Methods & Results

The team created an artificial intelligence (AI) program built on a neural network that can predict a patient’s probability of developing a cardiac arrest in the next ten years with statistically significant accuracy. The AI program views close-up images of patients’ cardiac tissues, and combined with the patient’s history, determines the probability of a cardiac arrest. The model was able to outperform human predictions of cardiac arrest, and the research team plans to implement the technology as a valuable tool available to physicians.

The research team modeled the AI after a neural network, which is a computer system modeled after the human brain. That is, “neural” pathways are strengthened by successful predictions of correlations in a given data set, enabling computers to make highly accurate predictions of increasingly complex and abstract concepts by applying its knowledge from these data sets.

The AI was programmed to conduct a personalized, patient-specific survival assessment, which analyzes a patients’ underlying conditions and vitals. Next, the team used contrast-enhanced cardiac scar images from and taught the AI to detect aspects of the image that are invisible to the naked eye by using neural network technology. Currently, cardiologists are only able to analyze parts of scar images such as volume, mass, shape, etc. These enhanced images are evaluated by the AI in quantitative ways that human doctors could simply never achieve. The AI was then tested on real patients and data from previous years to see if the neural network could use this data to reliably extrapolate it onto new data.

The researchers found that their algorithm could accurately predict cardiac arrest in real patients to a better extent than physicians. They also tested the AI at 60 different health centers around the US, indicating that this model could be replicated at other hospitals.


The researchers concluded that the AI could be of major use to physicians. They plan to continue development of the program for both cardiac arrest and other heart-related diseases. The technology could also improve the accuracy of other diagnostics that rely solely on visual observation. These findings have grand implications on the future of healthcare, indicating a new role of specialized software and artificial intelligence. It may not be long before this novel application of artificial intelligence becomes widespread among physicians, enabling improved patient care by revealing the previously unnoticed.


Mouse Study: Follicle-Stimulating Hormone Is a Key Instigator of Alzheimer’s Disease

A study published in Nature reports that Follicle-Stimulating Hormone (FSH) may be a key instigator of Alzheimer’s disease (AD). Treatment including gene therapy and anti-FSH antibodies reversed and prevented AD-related pathologies in mice.


Alzheimer’s disease (AD) is the 7th leading annual cause of death in the United States and is typically caused by an abnormal buildup of two proteins: amyloid and tau. Amyloid proteins help with neuron growth and repair but can destroy nerve cells later in life due to abnormal buildups referred to as plaques. Tau is intended to stabilize neurons by providing them with a  neurofibrillary structure. When unregulated, tau proteins can dysfunctionally aggregate into neurofibrillary tangles (NFTs), causing AD.

Elevated follicle-stimulating hormone (FSH) levels are often associated with menopause and, when regulated, the hormone’s intended purpose in the human body is to stimulate egg and sperm development. Both low and high levels of FSH are associated with infertility and sexual defects.

Scientists have long suspected that menopause plays a role in the pathogenesis of AD. During menopause, females have elevated levels of FSH, which—among other hormones—can lead to bone decay, weight gain, tiredness, and cognitive defects like those observed in AD patients. Women have a three-times higher rate of disease progression.

Method & Results

To determine if FSH is linked to the development of AD, researchers from Emory University and Icahn School of Medicine conducted an experiment in which they manipulated FSH levels in mice and then examined cognitive function and plaque formation.

In mice injected with extra FSH for three months, researchers already observed amyloid plaques and and tau NFTs, as well as inflammation and destruction of neurons—all symptoms of AD. Specifically, the damage, plaques, and NFTs occurred largely in hippocampal and cortical neurons. Further, these mice showed impaired spatial memory as demonstrated by comparatively poor performance in a water maze test. These changes were observed in both male and female mice.

The researchers then administered an antibody drug that lowers FSH levels. After treatment, they identified that the same AD symptoms identified in the FSH-supplemented mice had nearly disappeared. Their anti-FSH antibody (FSH-Ab) inhibited amyloid plaque and NFT formation while also reversing cognitive decline. As FSH-Ab inhibits all functions of FSH, the mice would also experience increased bone mass, decreased body fat, and increased energy expenditure.

The researchers believe excess FSH causes an increase in the expression of a gene that regulates an enzyme called arginine endopeptidase. This enzyme ultimately keeps amyloid and tau proteins in check, but once overproduced, clumping and tangling can begin. Experimentation revealed that either the deletion of this gene via gene therapy or FSH-Ab treatment successfully reduced amyloid plaques and tau NFTs and improved water maze performance.

While increased FSH levels in menopausal women is implied in the pathogenesis of AD, the researchers tested how male mice would respond to FSH-Ab. They found that the antibody “at the very least” prevented amyloid accumulation in the male mice.


The researchers concluded that extremely high FSH levels can impact protein regulation pathways, eventually leading to AD-related pathologies in mice. Also, antibody therapy targeting FSH can reverse and prevent formation of amyloid plaques and NFTs. Mice had recovered performance in cognitive tests following FSH-Ab treatment.

These findings have serious implications if the findings translate to humans. FSH concentrations could be used as an assay in patients to determine pathology and treatment. Antibody and gene therapies that proved to reverse and prevent AD symptoms in mice could also be used in humans with FSH-induced AD.

  • Abbott, A. (2022, March 9). Could drugs prevent Alzheimer’s? These trials aim to find out. Nature.
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  • Orlowski, M., & Sarao, M. S. (2021, May 9). Physiology, follicle stimulating hormone. National Center for Biotechnology Information.
  • Xiong, J., Kang, S., et al. (2022, March 2). FSH blockade improves cognition in mice with Alzheimer’s disease. Nature.