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Machine vision system is a branch of AI that is widely being adopted in the medical sector. This system is an assembly of computer hardware, software, and electronic components that enable automatic image-based imaging for medical diagnosis and treatment and surgical planning. This therapeutic analysis is done by analyzing medical images such as CT scans, X-rays, and MRIs that facilitate extraction of valuable information, including accurate diagnoses and early disease detection. As a result, machine vision system has emerged as an important tool in the healthcare sector, as it minimizes human errors and facilitates accurate and timely diagnosis.
Allied Market Research states that the healthcare machine vision system market is projected to garner a revenue of $4,500 million by 2031. The sector accounted for $617.40 million in 2021 and is estimated to exhibit a CAGR of 22.4 per cent from 2022 to 2031. This revenue growth is majorly attributed to factors such as increase in demand for accurate and timely diagnosis and alarming surge in prevalence of chronic diseases. The World Health Organization published a study in 2024 claiming that chronic diseases were responsible for death of approximately 43 million individuals in 2021. Among these, 18 million individuals were reported dead before the age of 70. This indicates the increasing need for machine vision systems for precise and early medical assessment. Moreover, rise in healthcare expenditure across countries and the modernization of healthcare infrastructure are boosting the demand for advanced healthcare technologies, including machine vision systems. In addition, integration of AI and deep learning algorithms in machine vision systems notably contributes toward the expansion of the industry. This is due to the fact that these technologies help in identification of abnormalities, which further facilitates timely diagnosis.
Understanding the benefits of machine vision system
The machine vision system offers a myriad of benefits; for instance, it offers high efficiency as it minimizes the risk of human errors and functions at a high speed, which results in enhanced effectiveness of the system. Moreover, unlike human workers, these systems do not suffer from fatigue or human error, ensuring greater consistency and higher product quality. Furthermore, by reducing the need for manual labour and minimizing material waste, machine vision significantly lowers operational costs. Since these systems do not physically interact with products, the risk of equipment damage is reduced, which helps minimize downtime. Moreover, by automating potentially hazardous manual tasks, machine vision contributes to a safer workplace for employees.
Assessing how machine vision systems are applicable in various medical fields
Machine vision systems are witnessing high adoption in the medical sector as they are applicable in detection of tumors, processing and analyzing medical images, and identifying irregularities. In addition, machine vision systems can identify and analyze irregularities in medical images at a faster rate as compared to doctors. This, in turn, results in timely detection of diseases, better treatment outcomes, and reduced financial burden of advanced treatments. Moreover, the system has the capability to analyze massive volume of datasets, which significantly reduces the risk of inaccurate diagnosis. Thus, various medical fields such as neurology, oncology, ophthalmology, cardiology, and orthopedics make use of machine vision systems.
In the field of neurology, machine vision systems exhibit the capacity to examine brain scans and diagnose conditions like tumours, aneurysms, or brain damage caused by Alzheimer's. On the other hand, machine vision systems enable early diagnosis in cancer care. In addition, cancer progression can be monitored, which helps in treatment and surgical planning. Alternatively, to accurately detect and treat eye-related diseases, including glaucoma, macular degeneration, and diabetic retinopathy, AI-powered imaging systems serve as essential tools. Furthermore, alarming increase in prevalence of cardiovascular diseases has boosted the demand for machine vision systems. This is attributed to the fact that these systems aid in detecting damaged tissue and blockages in heart images, thereby assisting in timely treatment of coronary artery disease. In the orthopedics sector, this technology can evaluate X-rays reports or MRI images to detect broken bones or diseases that cause joints and bones to wear. Thus, machine vision systems make it easier for doctors and healthcare professionals for timely diagnosis of health issues and choose the best treatments.
Components of machine vision systems
Major components equipped in machine vision system include lighting systems, image sensor, machine vision lenses, vision processing unit, and communication system. Lighting systems serve as the integral components of machine vision systems, as they play a crucial role in illuminating objects and identifying their distinct features. For instance, the technology helps in recognizing cancerous skin moles and identifying the existence of cysts and cancers in organs. To effortlessly inspect the objects, optimization of lighting parameters is necessary, including object, intensity, brightness, angle; distance between the camera, object, and light source; and the color, shape, and size. Various sources are used for lighting such as quartz halogen, LED, xenon strobe lights, and fluorescent. Back lighting, diffuse lighting, partial bright field lighting, and dark field lighting are the different lighting technologies used in machine vision systems. Contrarily, machine vision lenses capture images and transmit them to the image sensor of the camera. The system is either equipped with interchangeable lenses or fixed lenses, and many lenses exhibit color recognition feature.
On the other hand, the image sensor integrated in the machine vision system is meant for transforming the light into digital image. This is achieved by using a complementary metal-oxide-semiconductor technology or charged-coupled device for the conversion of photons into electrical signals, providing a digital image. To interpret the digital image captured by the sensor, the vision processing unit applies algorithms and subsequently sends it to the processing unit. Before initiating the analysis, the image is enhanced to focus on the important aspects. The unit recognizes and measures the required aspects during the analysis. These measurements are then compared with the standards for accurate decision-making. The decision is consequently transmitted by the communication system to the required machine components. On receiving the signal or data, the machine components adjust and control their actions depending on the output from the vision system. This interaction is carried out through discrete input/output signals or data transmission methods like RS-232 serial communication or Ethernet. These connections help the machine respond in real time, which makes the whole system efficient and accurate. As a result, each of these components are crucial for optimum performance of the machine vision system as well as appropriate and early analysis of diseases.
Conclusion
Machine vision system is gaining high traction in the medical sector as it finds its application in therapeutic analysis by interpreting CT scans, X-rays, and MR images, minimizes human errors, and facilitates timely and accurate diagnosis. Lighting systems, image sensor, machine vision lenses, vision processing unit, and communication system are the major components integrated in the system. It offers multiple benefits such as improved consistency, minimized interruption, cost efficiency, superior efficiency, and enhanced safety. High prevalence rate of chronic diseases as well as rise in need for precise and early disease diagnosis serve as the key driving forces for the increasing need of machine vision systems. With advancements in technology, AI and deep learning algorithms are being integrated in these systems, which will help in detection of anomalies and enable timely diagnosis. Thus, machine vision system is serving as a crucial tool in the healthcare sector.
(Author is an experienced content editor with nearly 10 years in the field)
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