EARLIER CANCER DETECTION Elsewhere, UK-based medical technology outfit Lancor Scientific has created an innovative AI-assisted spectroscopic device, with which it aims to achieve 90% accuracy in cervical cancer screening. As professor Paul Darbyshire, chief technology officer (CTO) at Lancor Scientific, explains, the company’s method is ‘unique’ by virtue of the manner in which its Tumour Trace OMIS (Opto- Magnetic Imaging Spectroscopy) device is used in tandem with sophisticated AI algorithms. “Te spectroscopy is driven by quantifiable quantum physics, that detects cancer in the first instance. Only having first established that there are cancerous cells, do the AI algorithms come in to play to classify the degree of progression of the cancer. Tis is significantly different from most ‘AI for cancer detection’ approaches, which use AI as the ‘first sweep’ for cancer,” he says. “Tose approaches run the risk of over- fitting and over-diagnosis, leading to over- treatments and worse patient outcomes. Our approach offers the best option for reducing the burden of cancer, by finding it early,” he adds.

DEEP LEARNING TOOL Ultimately, Rinke observes that AI technology ‘nicely complements’ conventional spectroscopy because it ‘fills a gap’ between experimental and theoretical spectroscopy. “Te objective of theoretical

spectroscopy is often to predict the properties of materials or chemicals. But

Another challenge relates to what

Todorović describes as the ‘inverse problem.’ For example, she points out that inverting the structure-spectra relationship is ‘not trivial, because the inversion might generate nonsensical structures or because the relationship contains non-invertible elements.’

“Both challenges are currently AI can assist in detecting cancer cells

this is a slow process that AI can speed up considerably. Experiments often have the opposite objective, to learn about a material or a substance by measuring spectra. “Te challenge is to interpret the spectra and to extract information from them. Tis is an inverse problem, inverting the map between structure and spectrum. Here AI can help again, inferring relations, features, patterns and properties from measured data. Te development of new technologies and new materials requires both fast predictions and intelligent inference,” he adds. However, in moving towards this goal,

Todorović stresses that, although humans are very good at analysing individual systems very deeply and thoroughly, they ‘cannot cope well with massive amounts of data’ – whereas AIs, in particular deep learning types, ‘thrive’ on large amounts of quality data, which ‘are not always available and would need to be generated.’

addressed in data science and machine learning communities,” she says. Moving forward, Todorović predicts that the key developments in this area in the coming years will be in the fields of materials design and interpretation of measured spectra – and she highlights the fact that ‘almost any materials scientist, be it in academia or R&D, frequently asks themselves the question what would be the best material to make their application or product better or what would give them entirely new functionality.’ “Te often ‘vague’ design criteria or specifications are currently hard to translate into concrete AI algorithms, but this will improve rapidly. Also the inverse problem will become easier to solve and then AI diagnostics could assist experimental spectroscopy,” she says. “For ARTIST, we envisage a universal

spectra predictor, a web-based deep learning tool that learns continuously and dynamically and evolves somewhat autonomously. Users can enter molecules or materials and will receive an instant spectrum prediction. ARTIST will eventually support different spectroscopies and encompass many materials classes,” she adds.


ancor Scientific’s blockchain- based AI tool will enable the early identification of uterine

cancer. It should provide 90% accuracy, whereas the precision of conventional Pap tests is only 60-70%. A research lab was set up in Graz, Austria for further research on the OMIS process. The company cooperates with the Graz University of Technology, the Medical


University of Graz and Sigmund Freud University in Vienna. Lancor established a subsidiary in Austria for this purpose. Research will be supported by public funding over a period of five years. The device itself is expected to be launched into the marketplace in 2019. “We have set out to make an accurate, cost-effective cancer identification technology. The confidence placed

in us by the Austrian government and the partnership with the specialists at the Graz University of Technology form the basis for continuing this journey and making a minimum of 10,000 devices available over the next five years. In this case, 500,000 cancer tests can be carried out each day,” says chief medical officer at Lancor Scientific, Roland Schlesinger. l

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