Medical image is a foundational element in the healthcare department, as it can reveal the internal anatomy of patients. Medical images have information that doctors may not be capable of finding directly, and often medical image databases are very huge to be correctly analysed by humans.
At present, healthcare providers are overpowered with huge amounts of data, starting from wearable device outputs to electronic health records. Yet, with this much amount of data comes the challenge: How to handle it correctly?
The important part here is not just collecting data but efficiently understanding and using it. Hence, in such a situation, the concept of vector search gives a ray of hope. By finding similar vectors in the datasets, vector search can find insights and patterns that might remain hidden.
Understanding Similarity Search
Similarity search is a method that helps to retrieve items based on their similarity traits, rather than just by their exact match. In other words, The retrieved objects may not be an exact match but rather are somewhat similar to the query object based on some predetermined criteria.
One of the most important advantages of similarity search is that it helps you identify objects which are alike to the query object, even when the object isn’t exactly defined. This is useful in apps such as recommendation systems, where the end goal is to recommend services or products that are alike to the ones you have previously liked.
However, similarity searches have some limitations. For instance, it can be very intensive, especially when you are dealing with high-dimensional data. Moreover, it can also be a sensitive thing to the choice of similarity measure and kind of data being searched.
The use of vectors in representing medical images
The main aim to build concepts in medical images with vectors is to enhance the output. There’s a concept called MCV’s or Medical COncepts Vectors that helps to improve the encoded vectors as an input. In other words, MCV’s force a NLP technique called Skip-gram.
The Skip-gram model is employed to forecast the context based on a given target word. Typically, the model reverses the roles of contexts and targets, aiming to predict each context word using its corresponding target word. In the context of Medical Concepts Vectors, we utilise the Skip-gram technique to generate vector representations for diagnosis, medication, and procedure codes. Collectively, these codes for diagnosis, medication, and procedures are referred to as Medical Codes or Concepts.
Applications of Vector Databases and similarity search in Healthcare
Wearable device data analysis
The propagation of wearable devices like smartwatches and fitness trackers has produced a huge amount of multi-dimensional health data. Similarity search can find the pattern and vector database can store, manage, and analyse data such as physical activity, sleep patterns, and heart rate. THis enables healthcare people to deliver personalised health recommendations, monitor overall health, and find early signs of health issues.
Patient similarity analysis
Finding similar patients is very important for personalised treatments and group analysis in clinical research. Vector databases can be used to store and manage high-dimensional features vectors reflecting patient profiles. This includes different health attributes like medical history, lab results, and demographic aspects.
By handling similarity searches and clustering, vector databases help healthcare professionals to find similar patients, enhance treatment plans, and speedup research efforts.
Medical imaging analysis
Medical imaging like CT, X-ray scans, or MRI produce huge amounts of high-dimensional data that represents the internal structure of our body. Vector databases are used to store and manage feature vectors taken out from medical images, licensing effective similarity searches, pattern recognition, and image retrieval. This can act as an aid in tasks such as treatment planning, monitoring patient progress, and automated disease diagnosis.
Genomic data analysis
Genomic data analysis is important for understanding the basis of genetics of diseases and targeted therapies. Vector databases will store and manage multi-dimensional representations of genomic sequences or features. They always facilitate analysis, querying, and visualisation of genomic data, identifying disease-causing mutations or identifying similar genetic patterns in individuals.
Epidemiological Modelling and Disease Surveillance
Vector databases play an important role in apps related to public health, including epidemiological modelling and disease surveillance. These databases store and manage multidimensional data including things like disease transmission patterns, risk factors, and population characteristics.
By using vector databases, the public health community gains the ability to analyse and visualise public health data efficiently. This, in turn, empowers public health officials to closely monitor disease outbreaks, pinpoint high-risk regions, and devise precisely targeted intervention strategies.
Healthcare Provider Performance Analysis
In the universe of healthcare administration, vector databases prove invaluable for the storage and management of high-dimensional data reflecting the performance of healthcare providers. This data includes important aspects like treatment efficiency, patient outcomes, and patient satisfaction. Vector databases excel in the effective handling of tasks such as similarity searches, clustering, and other operations. These capabilities enable the identification of best practices, the detection of performance irregularities, and the facilitation of data-driven decision-making within healthcare management.
Case studies of hospitals and research institutions implementing similarity search
Various research institutions and hospitals have implemented similarity search to enhance their research, patient care, and healthcare practices. Here are some case studies of similarity search implementations:
Mayo Clinic
Mayo is one of the leading healthcare organisations. It has implemented similarity search in the radiology department. This healthcare organisation uses similarity search algorithms to gain historical patient images that are similar to the present images. Similarity search has improved the efficiency and accuracy of diagnosis and has been very valuable in emergency cases.
National Institutes of Health (NIH)
The National Library of Medicine at NIH uses similarity search to manage and gain medical images. The implementation enables the researchers to find visually similar images in the vast database for research purposes.
Massachusetts General Hospital
Massachusetts General Hospital’s Radiology department has been using similarity search in their Picture Archiving and Communication System. This system allows the radiologists to search for similar cases to the one they are presently reviewing, helping in looking for relevant medical images as fast as possible.
Google Health
Google Health uses similarity search methods to improve medical image analysis. Their research and development include building deep learning models for finding diseases from medical images and looking for similar cases from a huge dataset.
Stanford University
Radiology research department at Stanford University has developed a deep learning-based similarity search technique. They use CNNs to encode and find visually similar medical images. This system is used in different research projects like identifying rare diseases from radiological images.