Empowering Breast Cancer with AI Prediction Applications in Healthcare

Breast cancer is a prevalent and potentially fatal disease, becoming increasingly common amongst both women and men. This disease is most commonly found in women, affecting a vast demographic. In 2020, the World Health Organization estimated that there were 685,000 breast cancer-related deaths globally.

When it comes to treating and managing breast cancer, knowledge is key. Unfortunately, cancer symptoms aren't always prevalent in the beginning; it takes an understanding of how cancer exhibits itself and preventative measures to identify it in its early stages.

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Early detection has the highest chance of survival, and the stage in which cancer is discovered dictates what treatment plant will be most effective. In the past, allopathic medicine and self-performed examinations have been the only tools available for identifying cancer, but in the most recent years, more tools have been made available.

As you've probably seen in the media, AI applications and tools are being utilized in almost every industry, including healthcare. We're still years away before AI doctors and surgeons are the norm, but there are currently many helpful applications that aid in discovering health issues early on.

These tools have opened new horizons in the early detection and management of diseases, including breast cancer. As an AI modeled health management app, start up (tallywell), we’re excited about the promises of incorporating AI into healthcare, both professionally and in our personal lives.

Traditional Breast Cancer Detection

Breast cancer can first appear as a lump, pain, or nipple discharge in some cases. After any of these symptoms are present, a mammogram is performed to determine whether or not cancer is present.

A mammogram is an x-ray examination of the breasts used to diagnose these symptoms.

Self-examination is one of the most effective tools you can use to ensure breast cancer is caught early on.

Even though self-examination is accessible to most people, having time to regularly perform this task can be challenging for some. Hence, the desire for even more convenient tools for breast cancer prediction is on the rise.

How can AI Predict Breast Cancer?

When it comes to AI breast cancer prediction, it starts with acquiring a vast dataset of mammogram X-ray digitized images. The dataset contains both images of positive results as well as negative.

Through machine-learning technology, these images are intricately examined, and certain characteristics are extracted and used to identify patterns in patient cases.

Other data, such as genetics, demographics, previous diagnoses, and targeted information on the breast lump or pain, are also included. When properly executed, an AI model uses a binary classifier system to determine with high probability whether a patient has breast cancer or not.

The four classifiers include TP (true positive), FP (false positive, FN (false negative), and TN (true negative). The most common prediction for breast cancer patients is FN, false negative. That's because some malignant cases haven't progressed far enough or are undetected by traditional measures.

AI's Role in Enhancing Breast Cancer Predications and Treatments

AI plays a vital role in the treatment and early detection of breast cancer, becoming one of the most effective tools used by both doctors and patients. What can AI do to help combat breast cancer? Well, it's now a matter of what AI can't do.

AI can be used to help facilitate surgery, chemotherapy, radiation therapy, hormone therapy, and immunotherapy. The Nearest Neighbor Classifier, Naive Bayes Classifier, Support Vector Machine Classifier, Adaboost and XGBoost, and Convolutional Neural Networks (CNN) are a few of the most notable models used in this realm of medicine.

According to the article by Jafari, Zahra, and Ebrahim Karami. 2023. "Breast Cancer Detection in Mammography" studies have found that a multi-layer CNN can classify magnetic resonance images as malignant or benign tumors using pixel information and online data augmentation.

How AI Can Enhance Your Daily Health and Wellness

How often do you monitor your daily exercise or sleep? While AI can be used to predict breast cancer and other life-threatening diseases, there are plenty of other useful ways you can incorporate AI into your daily health regime.

Tallywell is an AI health management app that monitors your biometric data directly from your Apple Watch. TallyWell monitors your steps, heart rate, sleep, blood pressure, weight, calories burned and other factors to curate a personalized health risk score in the same manner that FICO® provides consumers with a financial risk score and details (i.e. a credit score and report).

Your tallywell score determines your health risk.  That is, whether you’ve been making health positive decisions, or if lifestyle changes are necessary to reduce the possibility of acute and chronic conditions.

Over time, Artificial Intelligence and Machine Learning techniques and methods are employed to predict your tallywell score and deliver actionable recommendations to increase your tallywell score and maintain a preventative health regimen. TallyWell offers a bridge between traditional disparate health metrics and daily lifestyle habits in order to understand your current state of health and avoid potential health risks that may be on the horizon.

Just as self-examinations are essential in preventing the progression of breast cancer, your tallywell advises on how to promote and maintain a healthy lifestyle.

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