Dr Nitin Sood, Senior Director-Medical Oncology, Bone Marrow Transplant, Cancer Care, Medanta, Gurugram explains how AI and ML are rapidly transforming the medical field, offering innovative solutions for complex diagnostic challenges
Blood cancer is an increasing health concern in India, affecting thousands of individuals every year. Every five minutes, someone in India is diagnosed with blood cancer, with an estimated 70,000 deaths recorded annually. The most common types of blood cancers in the country are leukemia, lymphoma, and myeloma, each with distinct characteristics and treatment approaches. Leukemia alone accounts for 7.1 per cent of cancers in men and 6.4 per cent in women, and it is the most prevalent cancer among children, comprising 28 per cent of all pediatric cancer cases.[1]
Blood cancer in children presents unique challenges, as early symptoms like fatigue, fever, and bruising are often mistaken for common childhood illnesses, leading to delayed diagnosis. This delay can impact treatment success and long-term outcomes. However, advancements in image recognition and machine learning (ML) are transforming the diagnostic landscape, enabling earlier detection and improved prognosis. These technologies enhance diagnostic speed, accuracy, and accessibility, offering new hope to children battling blood cancer and their families.
The role of machine learning (ML) and image recognition in blood cancer diagnosis
AI and ML are rapidly transforming the medical field, offering innovative solutions for complex diagnostic challenges. AI involves the development of systems that mimic human cognitive functions, while ML, a subset of AI, enables computers to learn from data and make predictions or classifications with minimal human intervention. In the context of blood cancer, these technologies are being applied to analyse medical images, identify cancerous cells, and predict disease progression.
How it works
The diagnostic process begins with the collection of data, such as microscopic images of blood samples or genetic profiles. This data is then fed into machine learning algorithms designed to recognise patterns indicative of cancer. For instance, an ML model can differentiate between healthy and malignant cells by examining various features like cell shape, size, and structure. Unlike traditional methods, which require manual evaluation by pathologists, ML algorithms can process vast quantities of data in minutes, significantly reducing diagnostic time.
One of the most promising applications is the use of convolutional neural networks (CNNs) in image recognition. CNNs excel at analysing visual data, making them particularly effective for tasks like blood smear analysis. By continuously learning from new data, these models improve their accuracy over time, contributing to more reliable and consistent diagnoses.
Advantages of AI in blood cancer diagnosis
- Early detection: AI algorithms can analyse blood samples quickly and identify cancerous changes at earlier stages, improving survival rates and enhancing quality of life for children.
- Increased accuracy: By minimising the risk of human error, AI enhances the reliability of test results.
- Efficiency and accessibility: Automated systems can process large volumes of data swiftly, reducing diagnostic turnaround times and extending diagnostic capabilities to remote or underserved regions.
- Personalised insights: By examining genetic markers and disease progression patterns, AI helps tailor treatment strategies for individual patients, ensuring children receive the most appropriate care.
Additionally, AI tools must undergo rigorous clinical validation to gain the trust of medical professionals and regulatory bodies. Importantly, these technologies are designed to assist, not replace, human expertise—pathologists play a crucial role in interpreting AI-generated findings.
The integration of AI and machine learning into blood cancer diagnostics is poised to bring transformative changes to pediatric oncology care. Early diagnosis facilitated by these technologies can significantly improve survival rates and long-term health outcomes in children. As these tools become more sophisticated and accessible, they can help alleviate the emotional and physical burden on young patients and their families. By investing in awareness initiatives, advanced diagnostic solutions, and tailored pediatric care, India can strengthen its fight against childhood blood cancer, offering hope for a healthier future.
[1] https://pmc.ncbi.nlm.nih.gov/articles/PMC11254514/
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