Sofi­ane Boudaoud is a uni­ver­si­ty pro­fes­sor and research sci­en­tist work­ing with the C2MUST team at UTC-CNRS- BMBI. In par­tic­u­lar, he is study­ing the assess­ment of mus­cle aging using AI tools in both basic and clin­i­cal research.

“It’s a nat­ur­al process that affects both the anato­my, archi­tec­ture and phys­i­ol­o­gy of the mus­cle, but also its capac­i­ties, par­tic­u­lar­ly motor capac­i­ties. The neu­ro­mus­cu­lar sys­tem respon­si­ble for pro­duc­ing move­ment is also affect­ed. Age­ing is an inescapable process does not occur uni­form­ly in all indi­vid­u­als. “In an arti­cle pub­lished in The Jour­nal of Geron­tol­ogy Series A, my col­league Pro­fes­sor Kiyoka Kin­u­gawa, a neu­roge­ri­a­tri­cian at Charles-Foix Hos­pi­tal and an expert in func­tion­al explo­ration in the elder­ly, demon­strat­ed that a seden­tary lifestyle induces accel­er­at­ed mus­cle age­ing by com­par­ing the mus­cu­lar char­ac­ter­is­tics of peo­ple with reg­u­lar phys­i­cal activ­i­ty with those of seden­tary peo­ple in the same age group. Sar­cope­nia, the ulti­mate stage of aging, is the antecham­ber to loss of mobil­i­ty and falls”, he explains.

The use of AI not only enables us to study the mus­cu­lar aging process in greater detail, but also to detect risk-prone pro­files and per­haps even antic­i­pate the accel­er­at­ed aging process. A the­sis, as part of the Chronos project in col­lab­o­ra­tion with AP-HP, has been devot­ed to the pre­dic­tion of aging by AI. “We use spe­cial algo­rithms the aim of which is to find the rela­tion­ships between elec­tromyo­graph­ic sig­nals mea­sured on the sur­face of the mus­cle and age. We are cur­rent­ly final­iz­ing an algo­rithm capa­ble of pre­dict­ing a per­son­’s age class by study­ing this elec­tri­cal activ­i­ty alone. Anoth­er of the team’s projects con­cerns the devel­op­ment of a “dig­i­tal twin” of aging mus­cle. This is a mod­el that will mim­ic the mus­cle in all its behav­iours, in a real­is­tic, ana­lyt­i­cal and mul­ti-scale way. In this case, we’re going to train the AI to repro­duce the behav­iour of this “biofi­del­ic” mod­el to speed up sim­u­la­tions”, assures Sofi­ane Boudaoud.

Oth­er clin­i­cal projects with AP-HP are under­way. “The first, HIPRESM, is a PhD project aimed at devel­op­ing AI mod­els to pre­dict the abil­i­ty of elder­ly peo­ple to recov­er from hip replace­ment surgery. The sec­ond, Chronos SARC, focus­es on aging and sar­cope­nia. Prof. Kin­u­gawa is the clin­i­cal inves­ti­ga­tor, and I am the sci­en­tif­ic leader”, adds Sofi­ane Boudaoud.

MSD