Inteligență artificială pentru detectarea și cuantificarea bolii ficatului steatozic
Keywords:
Artificial Intelligence, Deep learning, machinelearning, fatty liver, ultrasound, hepatic steatosisAbstract
Prevalența steatozei hepatice este în creștere la nivel mondial. Deși metodele non-invazive de diagnostic, cum ar fi ultrasonografia și sistemele de scorificare clinică au fost sugerate ca alternative la biopsia hepatică, eficacitatea acestora a fost pusă sub semnul întrebării. Integrarea inteligenței artificiale (IA) cu metodele tradiționale de diagnosticare este în curs de explorare pentru a spori acuratețea abordărilor non-invazive. Cercetarea a utilizat baze de date bibliografice științifice PubMed, Scopus și Google Scholar. Termenii de căutare utilizați au fost „ficat gras”, „steatoză hepatică”, „inteligență artificială”, „învățare automată”, „învățare profundă”, „rețea neuronală convoluțională”, „rețea neuronală artificială” și „ultrasunete” etc. Review-ul sistematic a cuprins studii care au demonstrat că IA a avut un impact notabil asupra îmbunătățirii diagnosticului diferitelor afecțiuni hepatice, inclusiv a steatozei hepatice, steatohepatitei, fibrozei și cirozei hepatice. Prin intermediul analizei calitative s-a constatat că IA a fost deosebit de eficientă în îmbunătățirea acurateței diagnosticului pentru aceste afecțiuni. Integrarea sistemelor susținute de IA a demonstrat progrese promițătoare în detectarea și cuantificarea steatozei, steatohepatitei și a fibrozei hepatice la pacienții cu steatoză hepatică. Aceste sisteme au demonstrat capacitatea de a îmbunătăți performanța în diagnosticarea și evaluarea cu acuratețe a severității bolilor hepatice, oferind profesioniștilor din domeniul sănătății instrumente valoroase pentru un management clinic mai eficient.
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