Development and Validation of a Magnetic Resonance Imaging-Based Machine Learning Model for TMJ Pathologies

<div>The selection of <svg height="8.68572pt" id="M46" style="vertical-align:-0.0498209pt" version="1.1" viewbox="-0.0498162 -8.6359 9.95144 8.68572" width="9.95144pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M743 650H503L496 622L527 618C563 613 564 603 532 573C449 495 371 431 323 392C301 374 272 355 246 346L280 522C297 609 300 614 379 622L385 650H135L129 622C209 614 215 609 198 522L124 133C106 39 99 35 23 28L17 0H271L277 28C193 35 192 39 208 133L239 316C264 328 280 325 303 288C368 183 435 90 502 0H652L659 28C602 34 584 43 543 94C495 154 403 283 347 369L574 554C634 603 659 612 735 624L743 650Z"></path></g></svg> best methods to further evaluate the radiomic with <svg height="8.68572pt" id="M47" style="vertical-align:-0.0498209pt" version="1.1" viewbox="-0.0498162 -8.6359 8.02022 8.68572" width="8.02022pt" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink"><g transform="matrix(.013,0,0,-0.013,0,0)"><path d="M584 650H137L131 622C214 614 217 612 200 521L125 127C109 41 101 35 23 28L17 0H288L294 28C201 35 193 42 209 128L242 309H348C440 309 442 300 443 226H471L510 422H482C452 354 449 348 357 348H251L295 575C302 609 304 615 338 615H426C502 615 517 604 526 581C534 560 536 524 537 492L565 494C574 554 583 631 584 650Z"></path></g></svg> classifier scores for TMJ disc displacements.</div>

BioMed Research International

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Figure 7

Figure 7: Development and Validation of a Magnetic Resonance Imaging-Based Machine Learning Model for TMJ Pathologies 