“…In light of this fact [17], the approach that has been presented makes use of machine learning in order to identify the factors that are primarily [18] responsible for the failure of root canal therapy, such as a broken instrument, an overfilled cavity, a perforated root, or an underfilled cavity [4,6,7]. This paper also provides insights into the importance of [19][20][21] these variables for determining the tooth's survival time after root canal therapy (treatment longevity detection), which is not revealed in many studies.…”