Machine Learning and Intelligent Diagnostics in Dental and Orofacial Pain Management: A Systematic Review

Table 1

Summary outcomes of studies comparing diagnostic measures.

Author	Target condition definition	Testing sample size^a	Index test outcomes^b	Reference test outcomes^c
Cantu et al. [31]	Extent and infiltration of proximal caries into dentinal tissue	141	Sn = 0.75, Sp = 0.83	Sn = 0.36, Sp = 0.91
Endres et al. [5]	Detect and classify periapical inflammation	102	Sn = 0.51	Sn = 0.51
Kise et al. [13]	Diagnose Sjogren syndrome in parotid and submandibular glands	40	Parotid Gland; Sn = 0.90, Sp = 0.89; Submandibular Gland; Sn = 0.81, Sp = 0.87	Parotid Gland; Sn = 0.67, Sp = 0.86; Submandibular Gland; Sn = 0.78, Sp = 0.66
Yang et al. [15]	Detect the presence of pathologic growth	181	Sn = 0.68	Oral surgeons; Sn = 0.67; General dentists; Sn = 0.64
Kim et al. [39]	Localize periodontal bone loss and classify apical lesions	800	Sn = 0.77, Sp = 0.95	Sn = 0.78, Sp = 0.92
Kise et al. [12]	Identify fatty degeneration within the salivary glands	100	Sn = 1.00, Sp = 0.92	>3 years’ experience; Sn = 0.99, Sp = 0.97; <3 years’ experience; Sn = 0.78, Sp = 0.89
Krois et al. [6]	To detect the extent of periodontal bone loss	353	Sn = 0.81, Sp = 0.81	Sn = 0.92, Sp = 0.63
Murata et al. [14]	Identify features of sinusitis	120	Sn = 0.86, Sp = 0.88	>3 years’ experience; Sn = 0.90, Sp = 0.89; <3 years’ experience; Sn = 0.78, Sp = 0.75

Sn: sensitivity; Sp: specificity; ^aTesting samples: medical imaging data (radiographs/ultrasound/computed tomography); ^bIndex test: machine learning model; ^cReference test: human clinicians.