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Breakthroughs in radiology thanks to BayesiaLab
November, 15 2005 / press release
BayesiaLab, a data analysis software based on Bayesian networks, has allowed crucial medical breakthroughs and developments in interventional radiology. The highlighting of unthought-of correlations is at the basis of these advances. Bayesia Ltd. ’s idiosyncratic method of data analysis could easily be applied to other research fields, in radiology as well as in medicine in general.
Marc Legeais, assistant senior registrar at the radiology department of Tours University Hospital (Pr Herbreteau), France, has appealed to Bayesia’s experts to use their data analysis software in order to finalize his Ph.D thesis. Indeed, BayesiaLab’s unique features allowed his research to go much further than with any other data analysis tool. New reflection perspectives have emerged and been explored.
« BayesiaLab’s unsupervised analysis of our database has brought to light correlations we would never have thought of, or even researched ! That is the reason why this method is so innovatory and profoundly interesting.»
How did you get in touch with Bayesia ?
Marc Legeais : « At first, some members of the ESIEA (a school of engineering that provides education in the fields of Information Technology, Electronics and Automatic Systems, in which Lionel Jouffe used to teach and conduct his research) alluded to the company, and then I logged on to their website. I started to foresee the potential applications of Bayesia thanks to the extremely didactic nature of the website, with the possibility to download a great many Flash animations. »
Regarding your PhD thesis, what were you expecting ?
Marc Legeais : « I had to find a statistics tool that would shed a relevant light on the analysis of an huge databank. »
What did BayesiaLab bring ?
ML : « BayesiaLab allowed me to go much further than I initially planned. I could do a series of original applications: an intelligent imputation of missing data, search for correlations buried in my database, characterisation of identified variables (such as for instance the result of a therapeutic act or the number of recurrences after treatment).
On top of these applications, BayesiaLab offers us a real time simulation tool we can use during consultations to inform women about the «chances» of statistic evolution with respect to the initial variables we have (the number of fibromas, their size, the patient’s age, the number of children she has