Table 2.
Characteristics of the AI models proposed in the studies included
| Author (year) | Application | Imaging modality | AI technique | Workflow of AI model | Data set used to develop the AI model | Independent testing data set | Validation technique | Reference standard |
|---|---|---|---|---|---|---|---|---|
| Cephalometric landmarks | ||||||||
| Rudolph48
(1998) |
Localization of 15 cephalometric landmarks | Cephalometric radiographs | Spatial spectroscopy | Noise removal; Pixels labelling according to the edginess; Pixels connection and edges labelling; Localization of landmarks based on position or relationship to a labeled edge |
14 images from the department of orthodontics | NA | LOOCV | Expert’s localization |
| Liu49
(2000) |
Localization of 13 cephalometric landmarks | Cephalometric radiographs | Knowledge-based algorithm | Determination of the reference point (manual); Dividing the image into eight regions containing all 13 landmarks; Detection of the edges; Localization of the landmarks |
NA | ten images from the department of orthodontics | Independent sample validation | Expert’s localization |
| Hutton50
(2000) |
Localization of 16 cephalometric landmarks | Cephalometric radiographs | Active shape model | Searching the image near each point to find the optimum fit; Application of the required translation, rotation, scaling and deformation to the template; Repeat of the fitting procedure until average movement is less than a pixel; Generation of the final configuration and localization of the landmarks |
63 images from the department of orthodontics | NA | LOOCV | Expert’s localization |
| Grau51
(2001) |
Localization of 17 cephalometric landmarks | Cephalometric radiographs | Pattern detection algorithm | Localization of significant lines; Calculation of landmark Identification areas; Calculation of landmark models |
20 images | 20 images | Split sample validation | Expert’s localization |
| Rueda52
(2006) |
Localization of 43 cephalometric landmarks | Cephalometric radiographs | Active shape model; Principal component analysis | Using the template model to generate new images to fit the test image in the best way; Refinement of the match and localization of the landmarks |
96 images | NA | LOOCV | Expert’s localization |
| Sommer75
(2009) |
Measurement of 12 decision-relevant cephalometric angles | Cephalometric radiographs | An available orthodontic software (Orthometic®) | Localization of the landmarks and the soft tissue profile; Correction of the location of the landmarks (manual; optional); Calculation of the cephalometric angles |
72 images from 46 female and 26 male subjects aged 5–49 years | NA | Independent sample validation | Expert’s localization and measurement |
| Leonardi53
(2009) |
Localization of 10 cephalometric landmarks | Cephalometric radiographs | Cellular neural network | Assessment of relevant region in input image; Image processing by cellular neural networks templates; Landmark search by knowledge-based algorithms; Check of anatomical constraints; Output of landmark coordinates |
41 images from subjects aged 10–17 years | NA | NA | Expert’s localization |
| Vucinic54
(2010) |
Localization of 17 cephalometric landmarks | Cephalometric radiographs | Active shape model | Initial positioning of the active appearance model; Search through different resolution levels; Generation of the final convergence of the model and image |
60 images from subjects aged 7.2–25.6 years | NA | LOOCV | Expert’s localization |
| Cheng65
(2011) |
Detection of the odontoid process of the epistropheus | CBCT | Random forest | Extraction of features; Identification and localization of the odontoid process of the epistropheus |
50 images | 23 images | Split sample validation | Expert’s localization |
| Shahidi55
(2013) |
Localization of 16 cephalometric landmarks | Cephalometric radiographs | Template-matching algorithm | Using the template-matching technique consisted of model-based and knowledge-based approaches to locate the landmarks | NA | 40 images from a private oral and craniofacial radiology center | Independent sample validation | Expert’s localization |
| Shahidi58
(2014) |
Localization of 14 3D cephalometric landmarks | CBCT | Feature-based and voxel similarity-based algorithms | Adaptive thresholding and conversion to binary images; Volume construction; Centroid and principal axis calculation; Scaling, rotation and transformation; Volume matching; Landmarks transferring |
eight images from subjects aged 10–45 years | 20 images from subjects aged 10–45 years | Independent sample validation | Expert’s localization |
| Gupta59
(2015) |
Localization of 20 3D cephalometric landmarks | CBCT | Knowledge-based algorithm | Searching of a seed point; Find an empirical point through distance vector from the seed point; Define VOI around the empirical point; Detect a contour on the anatomical structure of VOI; Landmark detection on the contour |
NA | 30 images from the orthodontic treatment clinic database irrespective of age, gender and ethnicity | Independent sample validation | Expert’s localization |
| Gupta60
(2016) |
Localization of 21 3D cephalometric landmarks and measurement of 51 cephalometric parameters |
CBCT | Knowledge-based algorithm | Searching of a seed point; Find an empirical point through distance vector from the seed point; Define VOI around the empirical point; Detect a contour on the anatomical structure of VOI; Landmark detection on the contour; Cephalometric measurement |
NA | 30 images from the orthodontic treatment clinic database irrespective of age, gender and ethnicity | Independent sample validation | Expert’s localization and measurement |
| Lindner56
(2016) |
Localization of 19 cephalometric landmarks and classification of skeletal malformations | Cephalometric radiographs | Random forest regression-voting | Object detection; Principal component analysis to the aligned shape; Regularizing the output of the individual landmark predictors; Coarse-to-fine estimate the position of landmarks; Detection of cephalometric landmark positions; Calculation of the measurements between landmark positions; Classification of skeletal malformations |
400 images from 235 female and 165 male subjects aged 7–76 years | NA | 4-fold CV | Expert’s localization and classification |
| Arik57
(2017) |
Localization of 19 cephalometric landmarks and assessment of craniofacial pathologies | Cephalometric radiographs | Deep convolutional neural network | Localization of the image patch centered at landmark; Recognition of that pixel represents the landmark; Landmark location estimation; Refining the estimations; Landmark detection; Assessment of craniofacial pathologies |
150 images from subjects aged 6–60 years | 250 images from subjects aged 6–60 years | Split sample validation | Expert’s localization and classification |
| Codari61
(2017) |
Localization of 21 3D cephalometric landmarks | CBCT | Adaptive cluster-based and intensity-based algorithm | Initialization; Threshold optimization; Thresholding; Registration; labeling |
NA | 18 images from female Caucasian subjects aged 37–74 years | Independent sample validation | Expert’s localization |
| Montufar62
(2018) |
Localization of 18 3D cephalometric landmarks | CBCT | Active shape model | Computing coronal and sagittal digitally reconstructed radiographs projections; Initializing active shape model search by clicking close to sella (manual); Coronal and sagittal planes and landmark correlations; Definition of 3D landmarks in CBCT volume |
24 images | NA | LOOCV | Expert’s localization |
| Montufar63
(2018) |
Localization of 18 3D cephalometric landmarks | CBCT | Active shape model | Model-based holistic landmark search; Subvolume cropping; Knowledge-based local landmark search; Automatic 3D landmark annotation on CBCT volume voxels |
24 images | NA | LOOCV | Expert’s localization |
| Neelapu64
(2018) |
Localization of 20 3D cephalometric landmarks | CBCT | Template matching algorithm | Bone segmentation; Detection of mid sagittal plane based on symmetry; Reference landmark detection; Partitioning mid sagittal plane; VOI cropping; Extraction of contours; Detection of landmarks based on the definition on the contours |
NA | 30 images from the postgraduate orthodontic clinical database irrespective of age, gender and ethnicity | Independent sample validation | Expert’s localization |
| Osteoporosis | ||||||||
| Allen35
(2007) |
Detection of low BMD based on MCW | Panoramic radiographs | Active shape model | Identification of four points on the mandible edge (manual); Delineation of the upper and lower bounds of the inferior mandibular cortex; Measurement of MCW |
132 images from normal, osteopenic and osteoporotic female subjects aged 45–55 years | 100 images from 50 normal and 50 osteoporotic female subjects aged 45–55 years | Split sample validation | DXA examination and expert’s measurement |
| Nakamoto42
(2008) |
Diagnosis of low BMD and osteoporosis based on mandibular cortical erosion | Panoramic radiographs | Discriminant analysis | ROI selection (manual); Adjustment of the image position; Extraction of the morphological skeleton; Classification of normal cortex and eroded cortex |
100 images from normal, low BMD and osteoporotic female subjects aged ≥50 years | 100 images from normal, low BMD and osteoporotic female subjects aged ≥50 years | Split sample validation | DXA examination |
| Kavitha39
(2012) |
Diagnosis of osteoporosis based on MCW | Panoramic radiographs | SVM | ROI selection (manual); Image enhancement; Identification of cortical margins; Measurement of MCW; Classification of normal and osteoporotic subjects |
60 images from normal and osteoporotic female subjects aged ≥50 years | 40 images from normal and osteoporotic female subjects aged ≥50 years | Split sample validation | DXA examination and expert’s measurement |
| Roberts43
(2013) |
Diagnosis of osteoporosis based on cortical texture and MCW | Panoramic radiographs | Random forest | Identification of the mandibular cortical margins; Image normalization; Extraction of texture features and/or measurement of MCW; Classification of normal and osteoporotic subjects |
663 images from 523 normal and 140 osteoporotic female subjects | NA | Out-of-bag estimation | DXA examination and expert’s measurement |
| Muramatsu41
(2013) |
Diagnosis of osteoporosis based on MCW | Panoramic radiographs | Active shape model | Detection of mandibular edges; Selection of a reference contour model and fitting of the model; Measurement of MCW; |
100 images from 74 normal and 26 osteoporotic male and female subjects | NA | LOOCV | DXA examination and expert’s measurement |
| Kavitha38
(2013) |
Diagnosis of osteoporosis based on MCW | Panoramic radiographs | Back propagation neural network | ROI selection (manual); Segmentation of cortical margins; Measurement of MCW; Classification of normal and osteoporotic subjects |
100 images from normal and osteoporotic female subjects aged ≥50 years | NA | 5-fold CV | DXA examination and expert’s measurement |
| Kavitha37
(2015) |
Diagnosis of osteoporosis based on textural features and MCW | Panoramic radiographs | Naïve Bayes; k-NN; SVM | ROI selection (manual); Segmentation cortical margins; Evaluation of eroded cortex; Measurement of MCW; Analysis of textual features; Classification of normal and osteoporotic subjects |
141 images from normal and osteoporotic female subjects aged 45–92 years | NA | LOOCV/5-fold CV | DXA examination and expert’s measurement |
| Kavitha40
(2016) |
Diagnosis of osteoporosis based on attributes of the mandibular cortical and trabecular bones | Panoramic radiographs | NN | Extraction of attributes based on mandibular cortical and trabecular bones; Analysis of the significance of the extracted attributes; Generation of classifier for screening osteoporosis; Classification of normal and osteoporotic subjects |
141 images from normal and osteoporotic female subjects aged 45–92 years | NA | 5-fold CV | DXA examination |
| Hwang36
(2017) |
Diagnosis of osteoporosis based on strut analysis | Panoramic radiographs | Decision tree; SVM | ROIs selection (manual); Imaging processing; Analysis of texture features; Classification of normal and osteoporotic subjects |
454 images from 227 normal and 227 osteoporotic male and female subjects | NA | 10-fold CV | DXA examination |
| Maxillofacial cysts and tumors | ||||||||
| Mikulka33
(2013) |
Classification of follicular cysts and radicular cysts | Panoramic radiographs | Decision tree; Naïve Bayes; Neural network; k-NN; SVM; LDA | Cyst identification (manual); Cyst segmentation; Extraction of texture features; Classification of the jawbone cysts |
26 images from subjects with 13 follicular cysts and 13 radicular cysts | NA | 10-fold CV | Expert’s judgement |
| Nurtanio34
(2013) |
Classification of various maxillofacial cysts and tumors | Panoramic radiographs | SVM | Lesion segmentation (manual); Extraction of texture features; Classification of the lesions |
133 images from subjects with various cysts and tumors | NA | 3-fold CV | Expert’s judgement |
| Rana76
(2015) |
Segmentation and measurement of keratocysts | 3D images (MRI/CT) | An available navigation software (Brainlab) | Identification of keratocysts (manual); Lesion segmentation; Measurement of the lesion volume |
NA | 38 images from subjects with keratocysts | Independent sample validation | Expert’s segmentation and measurement |
| Abdolali67
(2016) |
Segmentation of maxillofacial cysts | CBCT | Asymmetry analysis | Image registration; Detection of asymmetry; Segmentation of the cysts |
97 images from subjects with 39 radicular cysts, 36 dentigerous cysts and 22 keratocysts | NA | NA | Expert’s segmentation |
| Yilmaz69
(2017) |
Classification of periapical cysts and keratocysts | CBCT | k-NN; Naïve Bayes; Decision tree; Random forest; NN; SVM | Lesion detection and segmentation (manual); Extraction of texture features; Classification of periapical cysts and keratocysts |
50 images from subjects with 25 cysts and 25 tumors | NA | 10-fold CV/ LOOCV | Expert’s judgement, radiological and histopathologic examinations |
| 25 images from subjects with cysts/tumors | 25 images from subjects with cysts/tumors | Split sample validation | ||||||
| Abdolali68
(2017) |
Classification of radicular cysts, dentigerous cysts and keratocysts | CBCT | SVM; SDA | Lesion segmentation; Extraction of texture features; Classification of lesions |
96 images from patients with 38 radicular cysts, 36 dentigerous cysts and 22 keratocysts | NA | 3-fold CV | Histopathological examination |
| Alveolar bone resorption | ||||||||
| Lin27
(2015) |
Identification of alveolar bone loss area | Periapical radiographs | Naïve Bayes; k-NN; SVM | ROI identification (manual); Fusion of texture features; Coarse segmentation of the bone loss area; Fine segmentation of the bone loss area |
28 images from subjects with periodontitis | three images from subjects with periodontitis | LOOCV/ Split sample validation |
Expert’s judgement |
| Lin28
(2017) |
Measurement of alveolar bone loss degree | Periapical radiographs | Naïve Bayes | Teeth segmentation; Identification of the bone loss area; Identification of the landmarks; Measurement of the bone loss degree |
18 images from subjects with periodontitis | NA | LOOCV | Expert’s judgement |
| Lee29
(2018) |
Identification and prediction of periodontally compromised teeth | Periapical radiographs | Deep convolutional neural network | Image augmentation; Extraction of texture features; Classification of the healthy and periodontally compromised premolars/molars |
1392 images exhibiting healthy/periodontally compromised premolars and molars | 348 images exhibiting healthy/periodontally compromised premolars and molars | Split sample validation | Clinical and radiological examinations |
| Periapical disease | ||||||||
| Mol31
(1992) |
Classification of periapical condition based on the lesion range | Mandibular periapical radiographs | Rule-based algorithm | Apex identification (manual); Analysis of texture features; Segmentation of the periapical lesion; Classification of the periapical lesion |
NA | 111 images exhibiting 45 healthy and 66 pathological mandibular teeth | Independent sample validation | Expert’s judgement |
| Carmody30
(2001) |
Classification of periapical condition into no lesion and mild, moderate and severe lesion | Periapical radiographs | A machine learning classifier | Apex identification (manual); Segmentation of the periapical lesion; Classification of the periapical lesion |
32 images exhibiting four different periapical conditions | NA | LOOCV | Expert’s judgement |
| Flores66
(2009) |
Classification of periapical cysts and granuloma | CBCT | LDA; AdaBoost | Lesion segmentation (manual); Extraction of texture features; Classification of the periapical cyst and granuloma |
17 images exhibiting periapical cysts or granuloma | NA | LOOCV | CBCT examination and histopathologic examinations |
| Multiple dental diseases | ||||||||
| Ngan74
(2016) |
Diagnosis of cracked dental root, incluse teeth, decay, hypoodontia and periodontal bone resorption | Dental X-ray images | Affinity propagation clustering | Extraction of dental features; Image segmentation; Extraction of features of the segments; Determination of diseases of the segments; Synthesis of the segments; Classification of diseases |
NA | 66 images exhibiting cracked dental root, impacted teeth, decay, hypoodontia or periodontal bone resorption | Split sample validation | Expert’s judgement |
| Son47
(2018) |
Diagnosis of root fracture, incluse teeth, decay, missing teeth and periodontal bone resorption | Intraoral and panoramic radiographs | Affinity propagation clustering | Extraction of dental features; Image segmentation; Extraction of features of the segments; Determination of diseases of the segments; Synthesis of the segments; Classification of diseases |
87 images exhibiting 16 root fracture, 19 incluse teeth, 17 decay, 16 loss of teeth and 19 periodontal bone resorption | NA | 10-fold CV | Expert’s judgement |
| Tooth types | ||||||||
| Miki70
(2017) |
Classification of tooth types | CBCT | Deep convolutional neural network | ROI selection (manual); Image resizing; Classification of tooth types |
42 images | ten images | Split sample validation | Ground truth |
| Tuzoff46
(2019) |
Tooth detection and numbering | Panoramic radiographs | Deep convolutional neural network | Define the boundaries of each tooth; Crop the image based on the predicted bounding boxes; Classify each cropped region; Output the bounding boxes coordinates and corresponding teeth numbers |
1352 images | 222 images | Split sample validation | Expert’s judgement |
| Others | ||||||||
| Benyó71 (2012) | Identification of root canal | CBCT | Decision tree | Classification of tooth type (manual); Image segmentation; Reconstruction of the shape of the tooth and root canal; Extraction of the medial line of the root canal |
NA | 36 images | Independent sample validation | Expert’s judgement |
| Ohashi45 (2016) | Detection of the maxillary sinusitis | Panoramic radiographs | Asymmetry analysis | Edge extraction; Image registration; Detection of maxillary sinusitis; Decision making (manual) |
NA | 98 images from 49 subjects with maxillary sinusitis and 49 subjects with healthy sinuses | Independent sample validation | Clinical symptom and CT images |
| Rana73
(2017) |
Identification of inflamed gingiva | Intraoral fluorescent images | A machine learning classifier | Segmentation of lesions; Classification of inflamed and non-inflamed gingiva |
258 images from subjects with gingivitis | 147 images from subjects with gingivitis | Split sample validation | Expert’s judgement |
| Yauney72
(2017) |
Identification of dental plaque | Intraoral photographs | Convolutional neural network | Segmentation of plaque; Classification of plaque presence/absence |
33 images taken from CD and 35 images taken from RD | 14 images taken from CD and 14 images taken from RD | Split sample validation | Expert’s judgement and biomarker label |
| De Tobel44
(2017) |
Classification of the stages of the lower third molar development | Panoramic radiographs | Deep convolutional neural network | ROI selection (manual); Feature extraction; Classification of the stage of the third molar |
400 images consisted of 20 images per stage per sex | NA | 5-fold CV | Expert’s judgement based on a modified Demirjian’s staging technique |
| Lee32
(2018) |
Detection of dental caries | Periapical radiographs | Deep convolutional neural networks | ROI selection (manual); Feature segmentation; lesion detection; Diagnosis of dental caries |
2400 images exhibiting 1200 caries and 1200 non-caries in the maxillary premolars/molars | 600 images exhibiting 300 caries and 300 non-caries in the maxillary premolars/molars | Split sample validation | Expert’s judgement |
AUC, area under the receiver operating characteristic curve; BMD, bone mineral density; CBCT, cone-beam CT; CD, commercial device; CV, cross-validation; DXA, dual-energy X-ray absorptiometry; LDA, linear discriminant analysis; LOOCV, leave-one-out cross-validation; MCW, mandibular cortical width; NA, not available; NN, neural network; PCT, periodontally compromised teeth; RD, research device; ROI, region of interest; SDA, sparse discriminant analysis; SVM, support vector machine; VOI, volume of interest; k-NN, k-nearest neighbors.