Computer Vision in Healthcare: Basics & 5 Key Applications

Understanding Computer Vision in Healthcare

In the first section, we’ll dive into the definition of computer vision in healthcare, how it works, and what role it holds for the medical industry’s future.

What is Computer Vision in Healthcare?

Computer vision is a subfield of artificial intelligence (AI) that enables machines to see and interpret visual data like images or videos. However, it can also understand text that appears within images (e.g., medical scans with attached text) by using advanced techniques like optical character recognition (OCR).

Computer vision might process a wide range of tasks, from simple ones (e.g., identifying edges) to complex ones (e.g., interpreting whole visual stimuli). Recent advancements in AI and big data have rendered computer vision more powerful.

Especially in healthcare, it leverages deep learning algorithms like convolutional neural networks (CNNs) and vision transformers to analyze medical images. These images can be X-rays, CT scans, or MRIs that provide impactful insights to detect early signs of disease, even before symptoms arise. With this capability, computer vision is widely adopted in different medical aspects, from medical imaging and physician-level diagnostics to pathology and ophthalmology.

With this technology, doctors can detect diseases early and personalize patient treatment. Meanwhile, healthcare providers can leverage computer vision to streamline their clinical operations.

How Does Computer Vision Work?

Computer vision in healthcare uses advanced algorithms and large volumes of data to train machines on how to perceive specific patterns within medical images. Training data here can involve images and even non-image data with labeled features (e.g., cancer cells and healthy cells). With deep learning techniques, the systems learn to detect these features in new data and refine their predictions over time. This allows computer vision tools to generate precise outcomes in real-world cases.

Various algorithms are used for computer vision, and one commonly used is the convolutional neural network (CNN). This algorithm can handle complex visual data in layers. As such, each layer of a CNN can scan different features (e.g., edges, textures, or shapes). For this reason, CNNs prove extremely useful in capturing fine details within medical images that humans might overlook.

Below are two primary computer vision tasks in healthcare: diagnostics and image generation.

How CNNs work in healthcare — Image Source: npj

a) When combined visual data with non-image data analyzed by general deep networks, CNNs can spot early signs of diseases more accurately, such as organ damage or tumors. This then helps medical professionals make informed decisions about a patient’s diagnosis or treatment.

b) Plus, computer vision models use CNNs to create new images for different purposes, including:

Improving the quality of medical images to see subtle details (e.g., minuscule tissue changes);
Segmenting particular parts (e.g., the boundaries around tumors) of medical images so that doctors can focus on the problem areas for precise diagnosis or treatment;
Generating realistic synthetic images to mimic rare health conditions or disease states.

Top 5 Computer Vision Applications in Healthcare

So, in which healthcare aspects is computer vision used? In this section, we’ll elaborate on the top five applications of this advanced technology in the medical industry:

1. Medical Imaging & Diagnostics

Today, the demand to use medical imaging technologies (e.g., X-rays, CT scans, or MRI) for effective and accurate diagnostics is booming. Further, a surge in chronic diseases (e.g., cancer, neurological ailments, or cardiovascular disorders) has highlighted the key role of these technologies in early detection and monitoring. These factors have encouraged the burgeoning growth of computer vision in medical imaging and diagnostics.

Previously, analyzing medical images could take hours, thus delaying diagnosis and treatment. But now, with computer vision, this process is much faster and more accurate. By using advanced algorithms, computer vision can detect abnormalities in those scans (e.g., spots on the lungs or blood vessel irregularities) that even human radiologists may hardly spot with the naked eye.

The analysis results are often returned in minutes, rather than hours or days. This allows doctors to take immediate action, especially in urgent situations where every second counts (e.g., stroke), and offer timely treatment that might save lives.

Until now, computer vision has been specifically adapted for chest X-rays, brain & abdominal imaging, disease detection, and segmentation (like marking ventricles). Further, this technology has been researched and applied in specific areas like cardiology, pathology, dermatology, and ophthalmology because these fields depend greatly on visual pattern recognition, which fits computer vision approaches well.

In ophthalmology, for instance, CNNs help diagnose eye diseases (e.g., age-related macular degeneration or diabetic retinopathy) and even other health risks (e.g., chronic kidney disease or anemia) from eye images.

Beyond diagnostics, computer vision can improve medical imaging by converting low-resolution images (e.g., noisy ultrasound) into higher-quality ones. It can even track changes (e.g., malignant lesions on skin) over time.

2. Surgical Guidance and Assistance

CV-based systems now support surgeons in many ways. First, they offer real-time information during operations, warn them about problem areas, and help them improve performance. To do so, computer vision will analyze live video feeds to guide surgeons through complex anatomy, making surgeries more accurate and less risky.

Second, this technology is often used in video-based laparoscopic and robotic procedures. In laparoscopic surgery, computer vision algorithms can detect and track how surgeons use tools in real-time. This helps surgeons ensure their right tool positioning, especially in complex operations where many tools can be used in tight spaces (like tissues), and minimize the risk of errors.

Through tool movement analysis, computer vision can assess a surgeon’s techniques based on predefined criteria, such as the Global Operative Assessment of Laparoscopic Skills (GOALS). This offers instant feedback to improve surgeon skills, enabling more standardized evaluations in training and real-world cases.

Further, computer vision makes a profound impact on robotic surgeries, especially in minimally invasive surgeries. Accordingly, instead of depending on traditional open procedures, surgeons can control surgical robots for more precise incisions and fewer human errors. These robots leverage tiny, high-definition cameras coupled with AI-powered vision to approach internal organs through small cuts.

The vision system can interpret visual stimuli, stabilize video feeds, filter out noise, and enhance image resolution to provide guidance. This helps surgeons focus on critical areas (e.g., tumor margins or blood vessels) without affecting other organs.

3. Telehealth and Remote Patient Monitoring

During the COVID-19 pandemic, social distancing became a new norm. Almost everyone, including patients, had to stay at home to avoid virus infection. This boosted the demand for online doctor consultations, virtual check-ups, and online prescriptions – all these activities are known as “telehealth.” Until now, even when the pandemic already went away, telehealth is still prevalent with a total of 13.7 million users estimated in 2028.

Computer vision, along with other AI techniques, has shifted how healthcare approaches people remotely. Through image and video analysis, CV helps doctors spot any unusual physical signs (e.g., breathing patterns or skin rashes) and suggest personalized treatments accordingly.

Computer vision also plays a crucial role in patient activity monitoring outside healthcare settings (e.g., hospitals or clinics). For elderly people or those at risk of falls, this technology might track movements to identify possible dangers, such as unstable walking patterns.

It also tracks daily activities (e.g., mobility or sleep patterns) constantly to recognize any suspicious changes that can significantly impact a patient’s health. Some AI-powered vision systems can even leverage thermal video data to detect movement and behavior without detailing a patient’s images. This therefore ensures the patient’s privacy.

In some cases, CV can track recovery progress by analyzing how visible injuries are healing or how patients improve their mobility. For example, computer vision monitors physiotherapy exercises for those recovering from conditions like a stroke. Further, it can support people with speech difficulties by analyzing their video footage to recognize sign language.

4. Neurological, Neurodevelopmental, and Psychiatric Disorders

Computer vision doesn’t only help with physical diseases but also supports treating neurological and psychological disorders. Several diseases like Parkinson’s or autism can show early signs through facial expressions or eye movement. For example, early signs of autism in children can appear in how their eyes contact or react to objects. Unfortunately, these early signs might not be easy to notice immediately.

With CV systems, however, doctors can recognize small, consistent changes in facial expressions and eye movement early. This allows them to intervene timely and begin treatments or personalized care plans soon before neurological conditions become worse.

Computer vision can also aid mental health care. Those struggling with anxiety or depression may show behavior changes that are hard to spot with the human eye. By analyzing facial expressions, posture, and movement, vision systems help identify early signs of these mental health issues. This enables better monitoring of psychiatric disorders and shows caregivers whether medication or therapy is effective.

5. Hospital Management

Besides disease detection and diagnostics, computer vision is changing how healthcare settings operate. Today, various hospitals are using “ambient intelligence” – an approach that offers a consistent, non-invasive way to monitor hospital activities using computer vision, combined with sensors and video streams.

In operating rooms, particularly, CV systems can track surgical steps to ensure workflows will be seamless. They can automatically document steps taken in emergency rooms during critical care (e.g., resuscitation) to ensure protocol adherence. These systems can even track whether healthcare workers are practicing hand hygiene or helping patients move safely in intensive unit care.

On a larger scale, hospitals leverage computer vision data to allocate resources and labor more effectively for optimal care. Through real-time data extracted from cameras, for instance, CV systems can manage stock levels of medications or surgical tools, track their movement, and identify when supplies are low. This allows for automated, real-time updates on inventory levels without depending greatly on manual counts. Also, this prevents the risk of essential items being out of stock or going past their expiry dates.

Future of Computer Vision in Healthcare

With the mentioned applications, the future of computer vision in healthcare is more promising than ever before. Accordingly, its global revenue is predicted to grow by 24% annually from 2024 to 2029, reaching a staggering value of $11.5 billion at the end of the projection period.

The coming of computer vision helps healthcare providers analyze diagnostic images more quickly and accurately. Further, this technology ensures that patients will receive appropriate care and comply with medication. This has increased the demand for computer vision systems in this realm. Besides, the regulatory initiatives proposed by the FDA and EMA have fueled this demand by offering a framework to verify and approve these systems in healthcare.

Looking forward, computer vision will likely become smarter. It’s all thanks to three key factors: 1) advancements in AI techniques, especially deep learning capabilities that can detect complex medical issues, 2) powerful GPUs that can process massive datasets and run complex calculations very quickly, and 3) the open-sourcing of vast labeled databases that are used to train those algorithms. All these factors make computer vision tools better at delivering accurate diagnostics and predictions.

Further, we’re witnessing the future of this technology increasingly relying on the cloud. Simplified deployment, high processing speeds, cost savings, and scalability are the key reasons behind the adoption of cloud-based CV systems. Indeed, cloud platforms like Microsoft Azure or Amazon Web Services allow your business to access important data in real-time and accommodate growing user bases without additional costs for infrastructure or maintenance.

Challenges of Computer Vision in Healthcare

Through the applications mentioned above, you might realize how beneficial computer vision is to healthcare. Indeed, this advanced technology helps streamline healthcare processes, improve disease detection & diagnostics, and customize patient care effectively. However, it still confronts some challenges that can hinder its wider adoption in the medical industry.

Refusal of Computer Vision Use

The first challenge comes from a clear resistance among healthcare professionals toward computer vision and other health technologies. One research on doctors from the USA, Portugal, and the Netherlands concluded that not many physicians are well-trained to use these technologies properly and they also don’t understand how AI decisions are made. Further, they believe that AI will reduce their autonomy and authority one day in the future.

Solution: It’s essential to build trust with AI use. Accordingly, healthcare settings must ensure that computer vision tools meet real needs and adapt to certain healthcare environments. To do so, they need to implement thorough clinical trials to test computer vision tools in real-world cases. This helps them measure the safety and effectiveness of these tools. Further, healthcare providers need to provide all staff involved with intensive training on the tools, allowing them to give feedback and tracking AI use regularly. This helps foster trust in AI use among medical professionals.

Lack of Curated Data

Another challenge lies in the shortage of curated data. We all know that the availability of structured and diverse datasets is crucial to train and fine-tune a reliable computer vision system.

Previously, these databases were manually inputted and primarily well-organized. But now, the coming of advanced technologies, typically IoT, enables the seamless collection of visual data from various healthcare appliances and sources (e.g., electronic health records).

However, much of this data isn’t of high quality and curated. Not to mention that they can contain biases that might impact the accuracy of vision systems in diagnosing patient conditions. This makes it hard to retrieve meaningful insights from the data.

Solution: Healthcare providers need significant workforce and time investment to curate unstructured data.

Further, it’s crucial to use diverse datasets and remove any biases to help develop fairer computer vision systems. Bias in data can be addressed in many ways; for example, expert discussions and labeling adjudication can help identify individual-level bias while missing data supplements and distributional shifts help the systems avoid population-level bias.

Ethical Issues

Finally, AI technologies, typically computer vision, still have unresolved ethical issues. Healthcare is one of the industries that often deal with highly sensitive information. The data is under strict privacy regulations like HIPAA, GDPR, or the HITCH Act that require healthcare providers to ensure privacy and security when collecting, storing, and using health data.

Besides, establishing responsibility for AI actions is another ethical challenge. In case diagnostic errors occur when computer vision supports diagnostics, who should be legally held accountable for these mistakes? Answering the question is crucial for ethical use.

Solution: To foster the ethical use of computer vision models in healthcare, hospitals must establish clear guidelines and frameworks. These frameworks should guide all staff on decision-making processes and regulatory compliance when working with health data. Also, techniques like federated learning can be adopted to keep data confidential while enabling the models to learn from.

There’s a fact that computer vision models aren’t always perfect and can perform poorly on new data. Therefore, planning for model limitations is necessary to handle any arisen problems. This involves transparent reporting, robust assessment, and continuous monitoring to ensure the models are responsibly used in healthcare.

Transforming Healthcare Experience with Designveloper’s AI Solutions

This article has highlighted the importance of computer vision in healthcare through its key applications. However, like any other technology, computer vision presents some challenges that require careful planning to resolve. In case you want to deploy AI tools across your healthcare facility, Designveloper is a reliable partner to cooperate with.

With over a decade of experience and expertise in software and AI development, we’ve developed scalable, customized solutions that align with our clients’ unique requirements. Our AI development services primarily revolve around the following areas:

AI Software Development: We adopt the latest AI advancements to create robust software solutions that can scale up along with your business growth and streamline your hospital workflows.

Generative AI: Our experts harness advanced algorithms to create innovative GenAI solutions specifically for your healthcare facility.

AI Chatbot Integration: We revolutionize your patient and staff experience by seamlessly integrating powerful AI chatbots into your existing systems. You don’t need to confront mundane tasks that can hold your communication processes in check.

ODC – Designveloper’s Telehealth Project

One of our successful projects is ODC. This France-based telehealth platform allows doctors to manage all their documents, bookings, prescriptions, and payments with patients. Meanwhile, patients can book appointments with private doctors, order medicine, and even have online check-ups.

Our team leveraged different technologies for backend (Python, Nodejs, and Golang), frontend (React, Angular), databases (Postgresql, MongoDB, Elasticsearch, and Redis), and a mobile app (React Native) to process large volumes of medicine, prescription, and patient data.

We also conducted demos using testing data (not based on real users) to test the software and had to ensure the software ensures adherence to healthcare industry standards including HIPAA and GDPR.

Partnering with Designveloper, you’ll upgrade healthcare workflows and experience to a new level.

If you want to further discuss your healthcare app idea, contact us now!