Posted 6th October 2022 by Nicholas Noakes
Digital health solutions in general have a poor track record of being sustained once they are implemented, often resulting in abandonment of the technology. Current digital pathology and artificial intelligence (AI) deployment strategies are generally too IT-focused – the technology being the focus, rather than the people and environment into which they are being deployed. Furthermore, a focus on technology alone will also hamper the effective integration of AI tools into the diagnostic workflow.
Posted 11th March 2022 by Nicholas Noakes
Fully digitising pathology operations has led to greater efficiency, cost savings, and quicker diagnosis for the Laboratory of Pathology East Netherlands (LabPON). The move, made six years ago, is showing measurable benefits and now, the institution is beginning to explore the potential of deep learning computational pathology algorithms, which might push the efficiency gains even further.Prof Alexi Baidoshvili
Conversation with Dr Hamid Tizhoosh, Founder of KIMIA Lab and Leading Expert in the Development of Unsupervised AI for Tissue Pathology
Posted 30th September 2021 by Nicholas Noakes
“What we have to learn from day one when we design these AI applications, is that pathology has to come with us. We cannot just design a network as computer scientists and then go to the pathologists just when we need to validate it. The pathologist has to be with us from the start.“
Dr Hamid Tizhoosh
Posted 31st August 2021 by Nicholas Noakes
“Digital pathology has reached the point where if you don’t have digitized slides, you will not be able to do six out of ten things that other pathologists can do today.”
Dr. Anil Parwani
Posted 25th May 2021 by Nicholas Noakes
Andrew Evans, speaking at the Digital Pathology & AI Congress USA, described new guidelines he helped to draft for validating whole-slide-imaging for diagnostic purposes. First published in 2013 the guidelines were designed to address the fundamental question, “what needs to be done to validate a whole slide imaging for diagnostic use?”. The review producing the new guidelines was published in May 2021.
Posted 30th March 2021 by Nicholas Noakes
Predicting the outcome of cancer can help the clinical decision-making process related to a patient’s treatment. The potential for Artificial Intelligence (AI) to support this was a key facet of the final keynote speech to the online 7th Digital Pathology and AI Congress: Europe, by Johan Lundin, Research Director at the Institute for Molecular Medicine Finland (FIMM) at the University of Helsinki and Professor of Medical Technology at Karolinska Institutet.
Posted 19th February 2021 by Nicholas Noakes
Deep learning tool predicts tumour expression from whole slide images
A deep learning model to predict RNA-Seq expression of tumours from whole slide images was among the industry innovations outlined at the 7th Digital Pathology and AI Congress for Europe. Created by French-American start-up Owkin, the detail of how the company’s HE2RNA model provides virtual spatialization of gene expression was detailed to online delegates by senior translational scientist Alberto Romagnoni who highlighted its use in clinical diagnosis. During his presentation, delegates heard how Owkin has collaborated with doctors, hospitals and academic institutions to develop the tool.