“Artificial Neural Networks to predict Infarcts in patients with Vague symptoms”.

Not all hearts give a thunderous warning. Some go out silently. A slight pressure in the chest. A cold sweat. An unexplained fatigue. Vague symptoms. So discreet that the body screams and no one hears it. Not the patient. Nor the doctor. That's where the science that doesn't blink AI comes in.

Infarcts and neural networks

Acute myocardial infarctions (AMI) remain one of the leading causes of death in the world, partly because many cases do not show classic symptoms and are detected too late. This is where artificial neural networks (ANNs), an advanced branch of artificial intelligence (AI), are evolving early diagnosis.

How do these neural networks work step by step?

1️⃣ Clinical data collection: ANNs receive large amounts of medical information, which can include vital sign records, electrocardiogram (ECG) results, blood tests (biomarkers such as troponins), medical images, and even patient demographics.

For a neural network to learn how to detect a heart attack, it first needs a lot of medical information from real patients.

Types of data:

• Vital signs: blood pressure, heart rate, temperature, etc.

• Electrocardiograms (ECG): records of the heart's electrical activity.

• Blood tests: biomarkers such as troponins, which indicate damage to the heart muscle.

• Medical imaging: such as echocardiograms or angiograms.

• Demographic data: age, sex, medical history, risk factors.

• Importance: The more complete and varied the information, the better the neural network can learn to identify subtle patterns that indicate risk of infarction.

2️⃣ Data preprocessing: Before feeding the network, this data is normalized and cleaned to ensure quality and consistency. This includes transforming electrical signals from the ECG into formats that the network can interpret.

Before the neural network can learn and make decisions, the medical data needs to be organized and “cleaned,” a process called preprocessing.

• Quality and consistency: Data coming from different sources (ECG, blood tests, vital signs) may have errors, be incomplete, or be in different formats. That is why it is necessary to correct them, fill in what is missing, and make sure they are uniform.

• Normalization: This means adjusting the values so that they are on the same scale and range. For example, converting all blood pressure measurements or biomarker levels to numbers that the network can easily compare, preventing one “too big” piece of data from dominating others.

• Signal transformation: ECG electrical signals are complex, raw data. They are transformed to extract important features and convert them into numerical formats that the network can “understand” and analyze.

• Why is this important? Without this step, the neural network could receive “dirty” or inconsistent data, causing it to learn poorly or make wrong decisions.

3️⃣ Neural network architecture: ANNs are made up of layers of artificial “neurons” that mimic the functioning of the human brain. Each neuron receives information, processes it with a mathematical function, and passes it on to the next layer.

Imagine a neural network is like a team of medical specialists organized into levels or stages, where each group has a specific job to analyze patient information.

• Layers: The network is made up of several “layers” that function like different teams. Each layer receives information, processes it, and passes it to the next layer for further analysis.

• Artificial neurons: Within each layer are many small units called “neurons” (not real cells, but mathematical computations that mimic neurons in the brain). Each neuron receives signals (data) from the previous neurons, processes them, and decides how much to “activate” its signal to send to the next layer.

• Processing: Each neuron takes the data it receives and applies a mathematical formula (a function) to it, which is how it evaluates whether a signal is important enough to be transmitted. This helps the network learn to detect complex patterns, such as subtle signals of a possible heart attack that a doctor might not notice as quickly.

• Information flow: Data enters the first layer (input layer), passes through one or more intermediate layers (hidden layers), where the heavy lifting of finding patterns is done, and finally reaches the last layer (output layer), which delivers a clear result, e.g., “high risk” or “low risk” of infarction.

4️⃣ Network training: The network is “trained” with labeled datasets (e.g., patients with and without infarction). During this process, the network internally adjusts millions of parameters (weights and biases) to learn to recognize complex, nonlinear patterns that associate certain data with the presence of an infarct.

Training a neural network is like training a resident physician to recognize subtle signs of disease from many real cases.

• Labeled data: The network is shown many examples of patients already diagnosed, where we know who had an infarct and who did not (these are the “labels”). Thus, the network knows which is the correct result for each case.

• Parameter adjustment (weights and biases): The network has millions of internal “buttons” that it controls to decide how important each piece of data is (as if they were clinical factors). During training, the network adjusts these buttons little by little so that its predictions are closer and closer to the actual diagnoses.

• Learning complex patterns: This is not just looking at an isolated piece of data (such as “Do you have chest pain?”) but combining many pieces of data and seeing complicated relationships that a human eye might miss. For example, how together certain minute changes in ECG, age, and fatigue can indicate risk of infarction.

• Iterative process: Training is like practicing on thousands of patients and receiving constant feedback to improve. The network “corrects its mistakes” over and over again until it is able to recognize patterns with high accuracy.

5️⃣ Validation and testing: After training, the network is tested on new data to evaluate its accuracy, sensitivity, and specificity, ensuring that it can detect real cases and minimize false positives or negatives.

After the neural network learns from the training data, it must be ensured that it performs well on new cases that it has not seen before.

• Testing with new data: It is presented with examples different from the ones it used to learn, to see if it can really identify well who has a heart attack and who does not.

• Accuracy: How correct the network's overall diagnosis is.

• Sensitivity: Measures how many real cases of infarction it detects correctly (avoiding false negatives, which would be patients with infarction that the network did not detect).

• Specificity: Measures how many healthy patients it correctly identifies (avoiding false positives, which would be healthy patients that the network marks as infarction).

• Why is it important? The network must be reliable, not only to get it right, but also to avoid alarming without reason or overlooking serious cases.

6️⃣ Real-time prediction: When a new patient comes in with symptoms, data is fed into the neural network. Based on what it learns, the artificial neural network analyzes subtle combinations of symptoms and clinical findings in seconds to predict whether there is a risk of AMI, even if the signs are not obvious to a traditional human diagnosis.

Once the neural network is trained, it can be used in real time, just when a patient arrives with vague or unclear symptoms.

• Clinical data entry: When a new patient is seen, vital signs, ECG, blood tests, age, etc., are taken and entered into the system.

• Automatic analysis: The neural network processes that information in seconds, comparing it with everything it learned before. It detects subtle combinations that could indicate risk of infarction, even if they are not obvious to the human eye.

• Immediate prediction: The system provides a probability or alert about the risk of acute myocardial infarction (AMI), helping the physician to act faster and with more confidence, especially in cases where symptoms are atypical.

7️⃣ Medical decision support: The system generates alerts and recommendations that help physicians prioritize studies, treatments, or interventions, reducing the critical time to save lives.

After making its prediction, the neural network does not replace the physician but intelligently supports him or her in making clinical decisions.

• Intelligent alerts: If it detects an elevated risk of infarction, the system can generate an immediate alert, drawing the attention of the medical team before symptoms worsen.

• Clinical recommendations: The system can suggest additional tests (such as an urgent cardiac enzyme or echocardiogram) or prioritize that patient's care over others in triage.

• Reduction of critical time: Every second counts in an infarction. This type of tool helps to act faster, avoiding delays in diagnosis and allowing timely treatment, which can make the difference between life and death.

In short, artificial neural networks function as invisible experts that detect hidden patterns in a sea of clinical data, helping to identify silent infarctions and improve early diagnosis, a technological advance that is changing the game in cardiology.

Simplified example in code: How does the neural network learn to detect infarcts?

This code shows a basic example of how a neural network can “learn” to identify patterns in medical data and predict the risk of infarction in new patients.

What is going on here?

1. Data definition the data are like the “clues” we give to the AI. In this example, each patient has 3 values: ECG result, troponin level, and age. The labels (0 or 1) indicate whether or not that patient had an infarction.

2. Preprocessing (normalization) Before feeding those data to the neural network, we “scale” them to be in a range from 0 to 1. This helps the network to learn better and not get confused with very large or very small numbers.

3. Creating the neural network Here, we define the structure of the model:

• A hidden layer with 5 neurons that process the information.

• An output layer with 1 neuron that gives a value between 0 and 1, which is interpreted as the probability of infarction.

4. Training The network "watches" the data and labels many times (50 cycles or epochs), and internally adjusts its parameters to learn to distinguish patterns indicative of infarction.

5. Prediction with a new patient When a new patient arrives with his data, they are normalized as before and passed to the model. The network calculates the probability that he/she will have an infarction.

6. Interpretation of the result If the probability is greater than or equal to 0.5, the AI alerts that there may be an infarction. If it is lower, it indicates low risk.

Technological tools for predicting heart attacks with AI

In the field of cardiology, artificial intelligence not only remains in theoretical predictive models but is already integrated into devices and platforms that help physicians and technicians to obtain faster, more accurate, and more accessible diagnoses.

These tools combine neural networks with imaging technologies, sound analysis, and real-time data processing to facilitate early detection of cardiac problems, even in remote or high-pressure environments such as ambulances and rural clinics.

Here are some of the most innovative technologies that are evolving the way heart attacks and other cardiac disorders are detected using artificial intelligence:

1. Caption Health

🔹 A platform that uses AI to guide cardiac ultrasound technicians in real time, ensuring perfect images are obtained for faster and more accurate diagnosis. This AI helps detect hidden cardiac problems that may indicate risk of heart attack.

1️⃣Descarga the app or access the official platform.

2️⃣Use the compatible ultrasound device connected to the app.

3️⃣La AI guides the technician or physician step-by-step in real time to capture cardiac images with optimal quality.

4️⃣Mientras the ultrasound is performed, the AI analyzes and highlights suspicious areas that could indicate risk of infarction.

5️⃣Los results and images can be easily saved and shared for diagnosis or specialized consultation.

2. Butterfly Network

🔹 A portable “ultrasound-in-your-pocket” device with integrated AI for fast, advanced cardiac imaging analysis anywhere from ambulances to rural clinics.

1️⃣ Purchase the Butterfly iQ or similar handheld device.

2️⃣Connect the device to your smartphone or tablet using the official app.

3️⃣Realize the ultrasound in the cardiac area following the indications of the app.

4️⃣La integrates AI processes and analyzes the images in real time, highlighting possible anomalies.

5️⃣Guarda and shares the results with specialists for diagnosis or follow-up.

3. Eko

🔹 Combines a digital stethoscope with AI to detect murmurs, arrhythmias, and other heart problems in real time, helping to identify heart attack risks from the basic consultation.

1️⃣Consigue the Eko digital stethoscope and connect the device to your smartphone o tablet.

2️⃣Durante the consultation, use the stethoscope to record patient heart sounds.

3️⃣La integrated AI analyzes the sounds in real time to detect murmurs, arrhythmias, and early signs of heart attack risk.

4️⃣Receive alerts and reports that facilitate fast and accurate clinical decision making.

5️⃣Comparte results with specialists or save the history for continuous follow-up.

4. Cardiologs

🔹 Platform that analyzes ECG with AI to detect arrhythmias and early signs of infarction in seconds, improving speed in the emergency department.

1️⃣Regístrate on its official platform.

2️⃣Sube the ECG file in a compatible format (usually standard medical files).

3️⃣La IA analyzes the ECG in seconds, pointing out anomalies and possible infarct risks.

4️⃣Receive detailed reports that can be integrated into the medical record to support medical decisions.

5. HeartFlow

🔹 Uses AI to create 3D models of coronary blood flow from images, helping to identify blockages that can cause infarcts.

1️⃣Se usually works in conjunction with the medical and imaging team, as it requires coronary CT scans.

2️⃣Las images are uploaded to the system to generate a customized 3D model of the blood flow.

3️⃣La AI analyzes blockages or narrowing that could lead to infarctions.

4️⃣Los results help to plan treatments or interventions, with clear visualizations for the physician and patient.

Artificial intelligence is not only evolving the early diagnosis of heart attacks, but is transforming the life expectancy of millions. With these technologies, the future of cardiology is more humane, accurate, and accessible than ever before. Tomorrow's heart is already beating with the power of AI!

Artificial intelligence: a powerful ally, but never a substitute for the physician

In this world where technology is advancing by leaps and bounds, it is vital to remember that artificial intelligence is not the lone star of diagnosis, but the best co-pilot a physician can have. AI can process mountains of data in seconds, uncover imperceptible patterns, and alert to hidden risks, but it can never replace the judgment, experience, and human sensitivity that only a healthcare professional brings to the table.

The real power lies in the collaboration between the unwavering precision of the machine and the empathy, clinical context, and intuition of the clinician. AI amplifies the ability of specialists to act quickly and with greater certainty, but the final word, the life-saving decision, will always rest in human hands.

Because at the end of the day, behind every algorithm there is a beating heart, a waiting family, and a human being who needs more than just data: he or she needs care, understanding, and humanity. Artificial intelligence is not coming to take the doctor's place, but to elevate him, so that together they can write a new era in medicine, where science and soul come together to protect what is most valuable: life.

The heart is not only a beating muscle; it is the guardian of our life, the symbol of our emotions, and the invisible center where science and hope intersect. Today, artificial intelligence does not come to replace it, but to protect it. Because every piece of data, every algorithm, and every prediction has only one purpose: to keep that heart beating... stronger, longer, and with less fear.

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