Introduction:
Cancer is a deadly disease characterized by cells dividing uncontrollably and spreading to other body parts. Therefore an early cancer diagnosis is necessary to treat the condition at an early stage, improve the patient's overall health and reduce the risk of cancer death. Traditionally diagnostic measures like blood tests, imaging tests, and biopsies are used in cancer diagnosis. But nowadays, the use of artificial intelligence in cancer diagnosis is garnering much popularity due to its more rapid and accurate diagnosis than traditional cancer diagnostic measures. However, it still has not entirely replaced standard cancer diagnostic measures in cancer diagnosis.
What Is Artificial Intelligence (AI)?
The term artificial intelligence was first coined by John McCarthy at the Dartmouth conference in 1956. Artificial intelligence is an integrated way of processing information and carrying out tasks through machines or robots blended with human intelligence. It is a wide-ranging tool that helps integrate information, analyze data, and comprehend useful insights and predictions that help improve decision-making. Artificial intelligence applications are used in various sectors like finance, gaming, data security, social media, entertainment, agriculture, etc.
How Does Artificial Intelligence Help in Identifying Cancer Cells?
In order to diagnose cancer, artificial intelligence uses spatial algorithms to analyze the data obtained from various cancer diagnostic measures such as MRI, CT scan, and blood tests. As a result, it helps detect cancer more rapidly and accurately than traditional cancer diagnostic measures. Besides cancer diagnosis, it is also used in treatment planning and patient monitoring, which leads to better health outcomes.
What Are the Different Concepts of Artificial Intelligence Used in Oncology?
The different concepts of artificial intelligence that are used in oncology include;
1. Machine Learning:
- Machine learning is a subfield of artificial intelligence that focuses on using data and algorithms to learn and make predictions with minimal human intervention. Machine learning algorithms are used in various applications such as medicine, speech recognition, email filtering, etc.
- Machine learning also helps detect cancer more accurately and rapidly using machine learning programs like the random forest, KNN (k-nearest neighbors), and SVM (support vector machines). For example, using breast cancer imaging, the random forest algorithm detects early-stage breast cancer.
2. Deep Learning:
- Deep learning is a subfield of machine learning that uses complex algorithms and deep neural nets to train a model. Unlike machine learning, deep learning can handle a large amount of unstructured data with the help of multilayered structures called neural networks. Deep learning applications are used in robotics, self-drive cars, and image colorization. Deep learning also helps detect cancerous tumors in the human body with much accuracy and aid in clinical decision-making.
- A deep learning model used in cancer detection is GAN (generative adversarial network). GAN is a type of data augmentation technique used in detecting breast cancer. It helps generate synthetic mammographic images from the digital database for screening mammography images. These synthetic mammographic images generated from GAN reduce the number of false-positive results of breast cancer screening.
3. Artificial Neural Network:
- The artificial neural network (ANN) is a deep learning model that draws inspiration from the human brain's neural structure. ANN are complex structures containing interconnected adaptive elements known as artificial neurons that can perform large computations for knowledge representation. They possess all fundamental qualities of the biological neuron system, including learning capability, failure tolerance, and generating ability.
- Artificial neural network technology helps diagnose cancer more effectively than traditional cancer diagnostic methods. It also helps in determining treatment plans and analyzing cancer prognosis.
- A type of artificial neural network used in cancer diagnosis is CNN (convolutional neural network). CNN uses U-net (a type of convolutional neural network architecture) to automatically segment the images of the prostate gland and prostate tumor lesions obtained from multiparametric MRI. As a result, it helps detect the malignant and benign abnormalities observed in images with high precision.
What Are the Various Applications of Artificial Intelligence in Cancer Diagnosis?
Artificial Intelligence Application in Radiographic Imaging:
Radiographic imaging is often used as a diagnostic measure in various cancers such as breast, colon, pancreatic, etc. The radiographic imaging techniques such as CT (computational tomography) scan, MRI (magnetic resonance imaging), X-rays, and mammograms provide images of the tumors that help radiologists to diagnose cancer. But sometimes, due to improper imaging and diagnosing errors by radiologists, false-positive results may be obtained, resulting in delayed prognoses and worse outcomes. Therefore, the application of artificial intelligence in radiographic imaging can help improve accuracy in cancer diagnosis.
Within cancer imaging, artificial intelligence performs three main clinical tasks:
1. Detection: AI helps localize objects of interest in radiograph images using a computer known as computer-aided detection (CADe). CADe aids in detecting any missed cancer in low-dose CT screening, locating any missed microcalcification in screening mammography, and helping detect brain metastases in brain cancer.
2. Characterization: Characterization broadly involves;
- Segmentation: Segmentation helps determine the extent of the tumor in two-dimensional and three-dimensional measurements.
- Diagnosis: Computer-aided diagnoses (CADx) are systems that use elements of artificial intelligence and image processing technology to analyze and evaluate medical images.
- Staging: The TNM staging system is the most standard staging system used in oncology. However, there are some limitations in TNM staging for some cancers. Artificial intelligence approaches are used to overcome the limitations of TNM staging, providing more accurate tumor staging and disease prognosis prediction.
- Imaging Genomics: Imaging genomics is an emerging discipline that correlates genomic data (such as gene mutation, gene expression, chromosomal alterations, etc.) with radiographic imaging for comprehensive tumor characterization.
3. Monitoring: Artificial intelligence can also play a vital role in monitoring tumor changes that occur in response to treatment.
Artificial Intelligence Application In Digital Pathology:
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Pathological examination through biopsies is most important in cancer as it gives a definitive diagnosis. Traditionally in a biopsy, the blood or tissue sample from the patient is taken and sent to the laboratory for pathological examination. In the laboratory, the sample taken from the patient is transferred to a glass slide, stained, and then examined under the microscope. The pathologist derives results by examining the sample, and once the results are obtained, the glass slides are archived manually in the hospital workspace. The traditional pathological examination has drawbacks like delayed and inaccurate results, difficulty accessing records and specimens, and being more time-consuming. However, with the advent of digital pathology, a much more accurate and rapid pathological examination becomes possible.
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In digital pathology, a virtual microscope is used that helps digitally convert microscopic specimens and transmits them over computer networks. The virtual microscope allows more detailed viewing of images, increases the convenience of accessing slides digitally, and provides more rapid results. Artificial intelligence application in digital pathology uses spatial algorithms to train computer systems to analyze the specimens more detailedly. As a result, it provides a more rapid and accurate diagnosis that helps in clinical decision-making.
Conclusion:
The use of artificial intelligence in cancer diagnosis is still in the nascent stages. However, artificial intelligence has shown numerous promising results in detecting various cancers. It provides a much-precision cancer diagnosis than traditional cancer diagnostic measures by analyzing a large amount of data through spatial algorithms. Due to improved cancer diagnosis by artificial intelligence, cancer treatment outcomes have become far better.
