This article covers computer vision in health care and presents a list of deep-learning applications for healthcare. We will review how deep learning in computer vision drives the development of new applications and end-to-end systems in key areas of medicine.
In healthcare and medicine, an immense amount of data is being generated by distributed sensors and cameras. The availability of data from medical devices and digital record systems greatly increased the potential of deep learning applications.
Deep Learning and Computer Vision
Deep learning is a subfield of machine learning that has seen a dramatic rise in popularity in the past 6 years. The deep learning trend is driven by increases in computational power (GPUs, parallelized computing) and the availability of massive new datasets.
The Impact of Deep Learning
Deep learning brought striking advances in computer vision that aims to make computers understand visual data. In traditional machine learning, domain expertise and human engineering to design feature extractors were needed to create learning algorithms able to detect patterns in data. In contrast, deep learning is a form of representation learning composed of multiple, sequentially arranged layers of representations.
The machine is fed with raw data and develops its own representations needed for pattern recognition. With deep learning methods, highly complex functions can be learned that achieve high accuracy in image recognition tasks.
Computer Vision in Healthcare
Computer vision focuses on image and video understanding. It involves tasks such as object detection, image classification, and segmentation. Medical imaging can greatly benefit from recent advances in image classification and object detection. Several studies have demonstrated promising results in complex medical diagnostics tasks spanning dermatology, radiology, or pathology. Deep-learning systems could aid physicians by offering second opinions and flagging concerning areas in images.
Convolutional Neural Networks (CNN) have achieved human-level performance in object classification tasks, in which a neural network learns to classify the object contained in an image. Those convolutional neural networks (CNN) have demonstrated strong performance in transfer learning, in which a CNN is initially trained on a massive dataset (e.g., ImageNet) that is unrelated to the task of interest and further fine-tuned on a much smaller dataset related to the task of interest (e.g., medical images).
Applications of computer vision in healthcare
- Application #1: Tumor Detection
- Application #2: Medical Imaging
- Application #3: Cancer Detection
- Application #4: Medical Training
- Application #5: Combating Covid-19
- Application #6: Health Monitoring
- Application #7: Machine-assisted Diagnosis
- Application #8: Timely Detection Of Disease
- Application #9: Remote Patient Monitoring
- Application #10: Lean Management in Healthcare
1. Tumor Detection
Computer vision and deep learning applications have proven immensely helpful in the medical field, especially in the accurate detection of brain tumors. Brain tumors spread quickly to other parts of the brain and spinal cord if left untreated, making early detection highly crucial to saving the patient’s life. Medical professionals can use computer vision applications to make the detection process less time-consuming and tedious.
In healthcare, computer vision techniques like Mask-R Convolutional Neural Networks (Mask R-CNN) can aid the detection of brain tumors, thereby reducing the possibility of human error to a considerable extent.
2. Medical Imaging
Computer vision has been used in various healthcare applications to assist medical professionals in making better decisions regarding the treatment of patients. Medical imaging or medical image analysis is one such method that creates a visualization of particular organs and tissues to enable a more accurate diagnosis.
With medical image analysis, it becomes easier for doctors and surgeons to glimpse the patient’s internal organs to identify any issues or abnormalities. X-ray radiography, ultrasound, MRI, endoscopy, etc., are a few of the disciplines within medical imaging.
3. Cancer Detection
Remarkably, deep-learning computer vision models have achieved physician-level accuracy at diagnostic tasks such as identifying moles from melanomas. Skin cancer, for instance, can be difficult to detect in time as the symptoms often resemble those of common skin ailments. As a remedy, scientists have taken the help of computer vision applications to differentiate between cancerous skin lesions and non-cancerous lesions effectively.
Research has also identified the numerous advantages of using computer vision and deep learning applications to diagnose breast cancer. Trained with a vast database of images consisting of both healthy and cancerous tissue, it can help automate the identification process and reduce the chances of human error.
With the rapid improvements in technology, healthcare computer vision systems may be used for diagnosing other types of cancer, including bone and lung cancer, in the near future.
4. Medical Training
Computer vision is widely used not only for medical diagnosis but also for medical skill training. At present, surgeons do not depend only on the traditional manner of acquiring skills through actual practice in the operation theatre. Instead, simulation-based surgical platforms have emerged as an effective medium for training and assessing surgical skills.
With surgical simulation, trainees get the opportunity of working on their surgical skills before entering the operation theatre. It allows them to gain detailed feedback and assessment of their performance, allowing them to better understand patient care and safety before actually operating on them.
Computer Vision can also be used to assess the quality of the surgery by measuring the level of activity, detecting hectic movement, and analyze time spent by people in specific areas.
5. Combating Covid-19
The Covid-19 pandemic has posed a massive challenge to the field of healthcare globally. With countries worldwide struggling with combating the disease, computer vision can significantly contribute to meeting this challenge.
Due to the rapid technological advancements, computer vision applications can aid in the diagnosis, control, treatment, and prevention of Covid-19. Digital chest x-ray radiography images, in combination with computer vision applications like COVID-Net, can easily detect the disease in patients. The prototype application, developed by Darwin AI, Canada, has shown results with around 92.4% accuracy in covid diagnosis.
Computer vision is used to perform masked face detection, which is widely used to enforce and monitor strategies preventing the spread of pandemic diseases.
To learn more, check out our article about 8 Computer Vision Applications for Coronavirus Control in 2021.
6. Health Monitoring
Computer vision and AI applications are being used increasingly by medical professionals to monitor the health and fitness of their patients. With these analyses, doctors and surgeons can make better decisions in less time, even during emergencies.
Computer vision models can measure the amount of blood lost during surgeries to determine whether the patient has reached a critical stage. Triton, developed by Gauss Surgical, is one such application that effectively monitors and estimates the amount of blood lost during surgery. It helps surgeons to determine the amount of blood needed by the patient during or after the surgery.
7. Machine-assisted Diagnosis
The advancement of computer vision in healthcare has led to more accurate diagnoses of ailments in recent years. The innovations in computer vision tools have proven to be better than human experts in recognizing patterns to spot diseases without error.
These technologies are beneficial to help doctors identify minor changes in tumors to detect malignancy. By scanning medical images, such tools can aid the identification, prevention, and treatment of several diseases.
8. Timely Detection Of Disease
For several types of diseases like cancer, tumors, etc., the life and death of the patient depend on timely detection and treatment. Detecting the signs early gives the patient a higher chance of surviving.
Computer vision applications are trained with vast amounts of data consisting of thousands of images that enable them to identify even the slightest difference with a high level of accuracy. As a result, medical professionals can detect such minimal changes that might have otherwise missed their eyes.
9. Home-based Patient Rehabilitation And Monitoring
Many patients prefer to rehabilitate at home after an illness compared to staying at a hospital. With computer vision applications, medical practitioners can provide patients with the necessary physical therapy and track their progress virtually. Such home training is not only more convenient but economical too.
In addition, computer vision technologies can also aid in remote monitoring of patients or the elderly in a non-intrusive manner. A widely researched area is computer vision based fall detection, where deep learning based human fall detection systems aim to reduce dependency and care costs in the elderly community. To read more about this topic, I recommend checking out our in-depth article about Fall Detection: A Vision Deep Learning Application.
Another method of patient monitoring with computer vision is the video-assisted analysis of standardized medical tests such as the TUG test (Timed Up and Go test). The computer vision system measures the time needed to perform a simple evaluative test to assess functional mobility. The TUG test can be used to estimate the risk of falling and the ability to maintain balance while walking.
Detect specific human poses such as Lying Down, Sitting, Standing automatically.
10. Lean Management in Healthcare
To properly identify a disease, a medical professional needs to spend a lot of time going over the reports and images to minimize the chances of error. But with the implementation of a computer vision tool or application, they can save a considerable amount of time while also getting highly accurate results.
Computer vision in healthcare helps hospitals to create maximum value for patients by reducing waste and waits. Queue detection, occupancy analysis, and people counting offer new tools to increase efficiency in healthcare. Unsurprisingly, many of those applications originally emerged in retail industries and are increasingly adopted by healthcare facilities to improve service quality and increase efficiency.
Area-based people counting in real-time using common surveillance cameras.
Computer vision has shown great potential in healthcare and medical imaging. However, as technology is advancing fast, more and more medical use cases have become possible. However, privacy-preserving deep learning and image recognition will be required to operate computer vision in health care applications.
Therefore, Edge AI will be an important technology, moving deep learning from the cloud to edge devices. By performing the ML tasks on-device, edge devices process video streams in real-time without sending sensitive visual data to the cloud.
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