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AI is not replacing pathologists but helping them cope with increasing workloads. Implementing AI into the digital pathology workflow can save a lot of time, time that can be spent on focusing on the most complex cases.
Over 5,000 pathologists, medical scientists, and researchers worldwide are using the Aiforia® Platform to make the most of digitized image analysis workflows and fully harness their expertise by automating repetitive tasks and increasing the speed and accuracy of case and study reviews. Let’s review some concrete examples from different industries.
Automated scoring and analysis save time and effort
Marika Karjalainen, a doctoral student at the Doctoral Programme in Clinical Research, University of Helsinki, and her research team compared the results of AI-assisted analysis to those done without assistance. For their study, whole slide images (WSIs) of routine H&E slides from 111 prostate cancer patients were digitized. The images were analyzed in two independent rounds: with and without the assistance of the Aiforia® Prostate Cancer Suite (4-week washout period), and a variety of statistical characteristics were calculated. Seven WSIs were also analyzed by 148-150 pathologists in 15 countries, and the consensus was compared to the AI-assisted result.
The study results show significant time-savings when using AI assistance: “Time spent for Gleason pattern analysis per slide was significantly reduced during AI-assisted diagnosis; on average, each slide took 34% less time (p < 0.05).”
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Faron Pharmaceuticals uses AI to perform spatial analysis in cancer drug development. Elisa Vuorinen, Research Project Manager at Faron, built an AI model to quantify and localize Clever-1 in the tumor microenvironment using Aiforia® Create.
“We performed spatial analyses on our sample images by measuring the distances from Clever-1 positive macrophages to tumor borders. Spatial analysis is not simple to complete systematically with a human reader. Aiforia’s AI solution assisted us in accurately extracting these measurements from our images with a relatively short turnaround time. Due to this, our team did not need to perform this repetitive and cumbersome task manually. We saved time and effort and were able to focus on other parts of our project."
The biggest benefit of using Aiforia for Elisa’s team is the time-saving that the automated scoring enables: “The automated scoring of Clever-1 positive cells sped up the pathological scoring of our tissue samples, which we are routinely staining as part of our ongoing clinical trials. Pathologists at a contract research organization currently do this work, and the availability of pathologists is often a limiting factor in obtaining timely results.”
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More similar case studies:
"This work shows that recent AI-based image analysis tools, such as the Aiforia Platform, provide valuable assistance in the field of image analysis and allowed us to drastically reduce inter-pathologist variability in the Ki-67 scoring of solid tumors. We were also positively surprised with the speed of execution of the Aiforia Platform, which was able to process regions of interest for all cores in just 2 minutes, saving us a lot of time and effort." –Rania Gaspo, Global Therapy Area Lead from Cerba Research
"Our AI models provide the objectivity of fully quantitative analysis that covers entire brain regions and time-saving through passive image acquisition and analysis, during which we can accomplish other goals." – Christopher Tulisiak, PhD, a Postdoctoral Fellow at the Van Andel Institute
Automated cell counting speeds up research
Cell counting is one of the most time-consuming and, frankly, the most tedious tasks in image analysis. As Nelli Sjöblom, an MD specializing as a pathologist at the Helsinki University Hospital, says, “If a person started counting these cells one by one, they would lose their health and age.”
Nelli created a deep learning AI model to evaluate novel prognostic biomarkers of primary sclerosing cholangitis (PSC), a chronic liver disease. She was pressed for time, and by selecting Aiforia’s deep learning AI, she did not have to commit to learning coding or how to use any complex software outside of her expertise. Nelli is now able to, for the first time ever, automate the detection of key biomarkers in PSC.
“And what’s more, the results are completely consistent. AI doesn’t have bad days like us humans do. Subjectivity is wiped away, while my work is significantly sped up. This is hugely important in pathology but also in research in general,” Nelli continues.
Read the complete case study here →
Merja Voutilainen and Mikko Airavaara are both based at the Institute of Biotechnology, and their research groups focus on developing novel treatments for Parkinson’s disease. The critical element in their research is to be able to quantify the number of neurons in the substantia nigra of experimental animal brains to find the neuroprotective efficacy of the drug molecules.
“We have experienced some challenges when quantifying dopamine cells from the substantia nigra,” Merja explains. “The current method is very limited by the time required to use the microscope and it is very limited in training a student in this and depends on how well and who trains the student. The variability between the persons to count the neurons is an issue,” Mikko adds.
To solve this issue, the research groups started using the Aiforia Platform to quantify the dopamine cells from the substantia nigra. Results are given instantly in both numerical and visual formats. “With stereology, it would have taken 45 minutes to count the neurons in one nigra area; with Aiforia, it took only 5 seconds”, they comment on the results.
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"If we had not had access to Aiforia, this analysis would have been much more time-consuming. It would be a lot harder; you could even say it would have been impossible to count these individual cells." – Miika Vuorimaa, Research Scientist at Orion Pharma
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"We decided to use AI for our projects because cell counting is very time-consuming. Besides, stereological methods provide just estimated numbers, which can lead to slightly error-prone analysis. Additionally, some stainings are very faint and difficult in the manual procedure. AI, like the one of Aiforia, helps to get rid of all these drawbacks and ensures a robust and efficient method to standardize and accelerate object counting." – Joan Compte, a predoctoral researcher at Vall d'Hebron Instituto de Investigación
“If you worked 24 hours a day and counted 1 object a second, it would take you a day and a half to count all those objects manually. So, assuming you work your normal 8 hours, you are still looking at counting every second of a whole day for a whole week. Your eyes get tired after looking at these images, and you start to second-guess yourself. The consistency across each of the sections was great with Aiforia; it doesn’t get tired.” – Can Kayatekin, Senior Scientist at Sanofi
See for yourself
To conclude, AI enables pathologists and scientists to review more cases in less time. Automating manual and repetitive tasks can not only reduce turnaround times in laboratories but also improve staff satisfaction, especially when AI can handle tedious tasks like cell counting.
Find out how to enhance your image analysis work in diagnostic pathology, preclinical studies, and medical research. Book a demo with one of our experts ↓
