AI in medicine and medical imaging is snowballing in growth. The industry was valued at $10.4 billion in 2021 and is expected to grow at a compound annual growth rate (CAGR) of 38.4% between 2022 to 2030. 

From automated robotic operations to enhanced medical imaging, AI is set to play a pivotal role in revolutionizing healthcare. 

Computer Vision in the Medical Industry

The medical industry has been one of the earliest adopters of AI technology, with a significant proportion of initiatives focused on AI diagnostics using computer vision. The future is a place where diseases and ailments can be identified dependably by computers using visual data. 

The World Economic Forum recently quoted that 90% of all medical data is image-based. In the last decade, a raft of academic studies investigated the potential of neural networking in deciphering complex medical images. 

Computer vision in healthcare has made major breakthroughs in the last few years. For example, Google DeepMind began collaborating with Moorfield Eye Hospital in 2016 and has now built numerous iterations of two deep convolutional neural networks that can diagnose eye diseases from retinal photographs and OCT scans. 

Meanwhile, mask-R convolutional neural networks (Mask R-CNN) have been used to detect malignant tumors with an overall accuracy of benign/malignant classification of 85% in one study of breast cancer tumors. Outside of cancer diagnosis, a neural network was able to diagnose pneumonia with an 86% success rate - the model is available as a web app, and the code is open source. 

Other studies have compared computerized diagnosis to human diagnosis, with results supporting model predictions. 

However, rather than replacing human workforces, this computer vision technology streamlines workflows and enables doctors to direct efforts toward prevention, treatment, and research. Computer vision provides a powerful and valuable second opinion without draining strained healthcare systems. 

Data Labeling For Computer Vision in Healthcare

Data labeling is required across the entire cycle of ML and AI in healthcare. 

• AI semantic segmentation is used to prepare complex MRI datasets of tumors, lesions, and other atypically-shaped features. 
• Polygon annotation is used in dentistry.
• Bounding boxes are used for definitively shaped objects of interest, e.g., kidney and gallbladder stones.

Data labeling for medical imaging requires significant skill and domain expertise. Labeling teams must liaise with doctors and diagnostics professionals to create the exceptional datasets necessary to train new generations of medical AIs. 

Sourcing Data For AI in Healthcare

Aside from labeling the data, one of the main bottlenecks of creating high-quality datasets for medical computer vision is sourcing the data in the first place. Medical data is often highly protected by privacy and confidentiality laws. Many datasets are built through cooperation with medical institutions, hospitals, and their patients. 

Gaining consent from patients to use their data is a challenge - Aya Data has already partnered with the Department of Radiology at the University of Ghana Medical Centre (UGMC) and are able to work with patients and professionals to help build quality datasets. 

Aya Data assists companies worldwide in training their AI diagnostic models, by creating high-quality labeled medical image and video datasets.

Services: Polygon, Bounding Box, Semantic Segmentation, Key Points

Domain Expertise: Radiology