AI and Machine Learning in Cancer Detection: Enhancing Early Detection and Diagnosis Through Technology
*ANCARANO, ITALY / ACCESSWIRE / March 2, 2024 /* Artificial intelligence is nothing new, but over the past few years, we've seen significant growth in this field. This technology continues to develop and now plays important roles in different industries, including the medical sector. Recently, massive breakthroughs have been made - allowing researchers and medical experts to actually take advantage of machine learning and AI to detect cancer at a very early stage. That could seriously improve the prognosis of this disease, so let's take a closer look at how this all works, and what we can expect going into the future.
*The Role Of Early Detection In Cancer Prognosis*
Cancer is a dangerous disease, causing cells in your body to grow and divide uncontrollably. The National Cancer Institute estimates that more than 1.8 million new cases of cancer are diagnosed in the United States every year. Plus, over 600,000 people lose their lives due to this disease annually.
When it comes to the prognosis for cancer, one of the most important things that comes into play is the time of detection. Early diagnosis of cancer is something that can significantly improve the success rates of treatment options.
The major factor to keep in mind here is the fact that cancer continuously grows. In many patients, the disease becomes metastatic, which means it spreads to surrounding tissues or even remote parts of the body. Once cancer spreads, it becomes much harder to treat effectively, further stressing the importance that early detection plays.
When cancer is detected at an early stage, doctors can focus on killing the cancer cells before they cause large and invasive tumors. The treatment team can also use the treatments to prevent the cancer from spreading further.
*How Does AI And Machine Learning Fit In With Cancer Detection*
While early detection of cancer is incredibly important, there's a major barrier that patients have been facing. When cancer is still at an early developmental stage, it's usually asymptomatic. That means patients will often have no symptoms as the disease starts to develop - and by the time symptoms develop, the cancer has already started to advance.
According to one research paper, early detection of cancer following a recommended screening test only accounts for about 14% of cases. That means most patients get diagnosed with cancer only when they begin to experience symptoms. In some cases, it's an incidental finding while testing and diagnosing something else.
With technology advancing at a rapid rate, there's good news for the early detection of cancer. Artificial intelligence has been making waves recently, along with machine learning modules. Now, research has shown that the combination of these two technologies can be incredibly valuable in detecting cancer among patients.
To understand how this is possible, it's important that you realize how machine learning and AI work together.
In terms of cancer diagnosis, remember that your doctor keeps a record of your visits. If you have bloodwork done or any other tests, your doctor will add the data and the results to your health record. Even in cases of a biopsy sample that your doctor collects, any conclusions made from the test will be connected to your medical records.
Machine learning can analyze massive databases of data to identify patterns that relate to the development of cancer. The technology not only considers how cancer develops but also looks at cases where cancer was diagnosed previously in patients.
Thanks to these algorithms, AI can use the data that was provided to the machine learning module to detect possible cases of cancer - and this can help to ensure patients are diagnosed as early as possible.
*Current Research On AI And ML For Cancer Detection*
There are a couple of studies and research papers that have already taken a look at how AI and machine learning models can be used to improve cancer detection. A large focus of these papers is to give more clarity in cases where patients do not go for cancer screening often enough - especially those who are already at risk.
According to one paper, this entire approach uses a three-layer system. It starts with artificial intelligence at the surface, where computers mimic the behavior and intelligence of humans. However, the benefit here is the fact that AI can process data at incredible speeds. Data processing that would take humans hours or even days could be analyzed by AI in merely minutes.
At the center lies machine learning, where a model is constantly fed data and will improve over time. As the name suggests, it's a technology that continues to learn. Deep learning is another layer that the research paper focuses on - this is the deepest level of machine learning, where neurons, computers, and datasets are interlinked, allowing AI to be more comprehensive when assessing data.
A study in the Journal of Personalized Medicine focused on using deep learning and AI to detect breast cancer. Data from 1501 patients were collected, with a focus on mammography. However, the data was divided to ensure there was a control group. The sensitivity when humans analyzed the mammograms was recorded as 81%. Machine learning models, however, had a much greater sensitivity at 87%.
In another paper, researchers wanted to consider not only the accuracy of machine learning models but also classification errors. After eliminating some of the features that the researchers used in the machine learning model, the accuracy of the ML model increased significantly, making it a highly effective tool when comparing malignant lesions to benign ones.
*Conclusion*
AI, especially when combined with machine learning models, holds a lot of promise and opportunity for the medical industry. And, according to recent research, these tools may be incredibly valuable when it comes to detecting cancer at an early stage. While more research is still needed, current evidence suggests that it may be especially useful in detecting cancer of the breasts, liver, prostate, lungs, and brain.
*References*
https://www.cancer.gov/about-cancer/understanding/statistics
https://www.norc.org/research/library/new-research-highlights-just-one-in-seven-diagnosed-cancers-foun.html
https://privatehealth.com/the-silver-lining-of-an-incidental-cancer-diagnosis
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8946688/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7711783/
https://www.nature.com/articles/s43856-022-00199-0
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*SOURCE: *Dr. Valve LLC
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