Introduction
Hans Chiari was the first who defined the term Chiari malformation (CM) in 1891, specifying the malformation of varying degrees of the descent of the brain stem and cerebellum below the foramen magnum (FM) (1). The most common form of CM is type I, the elongation of the tonsils of the cerebellum into the upper cervical canal. Although this situation is usually presented as an incidental radiological finding, many symptoms may also guide the practitioner to such a diagnosis (2).
The prevalent belief is that CM-I could result from an intrinsically smaller overcrowded posterior fossa that predisposes the upper cervical spine to eventual herniation of the cerebellar tonsils, resulting in altered dynamics of Cerebrospinal fluid (CSF) flow (3). The known primary radiological diagnosis of CM-I is relied on the degree of tonsillar herniation (TH) below the FM. However, recent data also shows the association of such malformation with smaller posterior cranial fossa (PCF) volume and the anatomical issues regarding the Odontoid (4-7). Recent studies suggest more radiological criteria that may aid to the diagnosis of CM (5, 6, 7, 16, 18, 21).
Machine learning (ML) is one of the traditional methods of computer systems that help to recognize and detect patterns in organizational and commercial decisions (e.g., underlying dimensions/subgroups, nonlinear associations/patterns, and so on) (8). This situation can be used in differentiation, classifying, and organizing many health problems. ML offers an opportunity to overcome obstacles using conventional statistical methods.
This study presents the achieved results regarding some detected potential radiological findings that may aid CM-I diagnosis using several ML algorithms.