Skin conditions are one of the most common ailments that patients present primary care physicians with. They present a challenge because there are hundreds of potential diagnoses for very similar looking conditions. Access to specialist dermatologists is often limited meaning that doctors and nurses may not have all of the help necessary to make the right decisions. Computer vision based AI models are now being developed that have the potential to provide diagnostic support to clinicians trying to correctly identify skin diseases.
The raw material for this revolution is dermatology is precisely annotated training imagery for machine learning. Keymakr, is one of the professional annotation services that is offering high quality medical images for the purposes of skin disease annotation. This blog will explore the advances being made in this area and focus on the specific annotation techniques that are supporting this effort.
Streamlining diagnoses with AI
Patients often present with hard to diagnose skin complaints. Dermatologists, who are best placed to identify these conditions, tend to be in short supply, particularly in rural areas. Lack of support for primary care physicians or nurse practitioners can lead to misdiagnosis or late diagnosis. An AI tool that could mimic the expertise of a dermatologist would be a significant aide to clinicians in the field. A number of these models have been developed and have shown the capacity to provide reliable diagnostic support. In each case deep learning approaches were adopted in which a neural network was trained with tens of thousands of images from dermatology practice. Using this data the models are able to identify the subtle differences between multiple different conditions. Recently these systems have become able to distinguish between classes of condition within a particular diagnosis, and even suggest treatment options to physicians. These exciting developments promise to increase the speed and accuracy of skin condition care for all patients.
Annotation techniques and quality control are part of the solution
Professional annotation providers can provide the image data that makes medical AI possible. By deploying specialised annotation techniques alongside expert quality control processes annotation services can free up medical AIl researchers to innovate with this already promising technology:
- Polygon annotation tools can be deployed to accurately outline examples of skin disease in dermatology images. By connecting points around the affected area annotators are able to define the often irregular shape of skin conditions, allowing the model to identify and define them more successfully.
- Semantic segmentation annotation work by diving images into classes pixel-by-pixel. This technique allows annotators to classify healthy skin in contrast with potentially unhealthy areas. Granular detail of this nature is essential for the precise functioning of diagnostic technology.
- Professional annotation providers are usually rigorous when it comes to security. Strict standards must be followed in order to keep sensitive patient data and information private and secure.
- Services like Keymakr work with certified dermatologists who specialise with targeted skin conditions to ensure that all of the labels attached to annotated data are accurate. Precisely identified data is essential at this stage due to ensure high levels of competency in a final model.
- Quality control can be assured by cross-checking between clinicians. This data is then further verified by a senior dermatologist in order to ensure that no errors will make it through to the final dataset.
Professional annotation services ensure quality data
Correctly identifying serious skin conditions and diseases can make an enormous difference for individual patients. Diagnostic aids have shown the capacity to support doctors and nurses and save lives. These tremendous advances can be further facilitated by experienced medical image annotation providers.
Keymakr leverages proprietary annotation tools, and multiple layers of quality control to provide data annotation that is precise, affordable, and scalable.