Aiforia makes key advancement towards end-to-end clinical pathology workflow solution with the release of its CE-IVD marked Aiforia® Clinical Suite ViewerRead more
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In the past few decades, the use of Contract Research Organizations (CROs) has rapidly expanded alongside the boom of the biotechnology and life sciences industries. Currently, CROs are generating multi-billion dollar revenues for their conduct, planning, and execution of pre-clinical investigations and clinical trials. This number is projected to grow; according to a report by Kalorama Information, a market-research publisher in Rockville, Maryland, more than one-third of all global drug-discovery research will be outsourced to CROs by 2021.
To support the rapid development of the market, many CROs are searching for process optimization solutions to accommodate a larger client base. CROs are often asked to run preclinical trials involving a myriad of tasks, including utilizing various animal models and evaluating a large number of tissue samples. A great deal of manual labor is required from supporting scientists, from calibrating microscopes to extracting results by eye.
AI as a multi-functional solution
Artificial intelligence (AI) has allowed the digitized preclinical trial process to be optimized and automated, accelerating the time it takes to perform tasks as well as improving the consistency and accuracy of the results generated. From data collection all the way through tissue evaluation, AI serves as a powerful tool for CROs.
A prominent use case for AI is in digital pathology. AI models can assist pathologists in analyzing medical images for cancerous tissue, proteins, cell types, and more. AI models can not only detect subtle changes in images with high precision and efficiency, but also remain consistent throughout the many iterations required for preclinical research. Additionally, the use of AI helps to standardize analysis and therefore provide more accurate results.
Another important aspect of preclinical trials is research results validation, which requires evidence that the methods for obtaining results are robust, reliable, and reproducible. A well-trained AI model helps to facilitate exactly this. External validators are crucial to ensuring the input data, which ultimately determines the AI model’s results, is objectively accurate and reliable. This step in AI model training accounts for multiple professional perspectives prior to using the AI model, removing the subjectivity of traditional image analysis. The precise data generated by AI models provides scientists with the concrete numbers that they need to validate their results.
Aiforia Create allows pathologists to reap the benefits of using AI for image analysis. The Speciality Pathology Services unit located at the Charles River Laboratories (CRL) UK site is increasing the speed and accuracy of image analysis in pre-clinical toxicology studies with Aiforia's AI software and services. Read how Aiforia Create helped CRL automate the image analysis of lung fibrosis here.
In brevity, the use of AI in preclinical studies drastically decreases turnaround times and improves precision, leading to a more streamlined workflow. It removes human error and bias from the equation, helping scientists to produce research results faster and with more accuracy. CROs can continue to support their growing client bases with ease.