Table 3.
Author (Year) | Conclusion | Limitations (Risk of Bias *) |
---|---|---|
Okada [16] (2015) | The proposed model may assist clinicians to accurately differentiate periapical lesions. |
|
Abdolali [17] (2017) | The proposed model can improve the accuracy of the diagnosis of dentigerous cysts, radicular cysts, and keratocysts, and may have a significant impact on future AI diagnostic systems. |
|
Yilmaz [18] (2017) | Periapical cysts and keratocysts can be classified with high accuracy with the proposed model. It can also contribute to the field of automated diagnosis of periapical lesions. |
|
Lee [19] (2020) | Periapical cysts, dentigerous cysts, and keratocysts can be effectively detected and diagnosed with the proposed deep CNN algorithm, but the diagnosis of these lesions using radiological data alone, without histological examination, is still challenging. |
|
Orhan [28] (2020) | The proposed deep learning systems can be useful for detection and volumetric measurement of periapical lesions. The diagnostic performance was comparable to that of an oral and maxillofacial radiologist. |
|
Abdolali [29] (2019) | The proposed system is effective and can automatically diagnose various maxillofacial lesions/conditions. It can facilitate the introduction of content-based image retrieval in clinical CBCT applications. |
|
Johari [30] (2017) | The proposed deep learning model can be used for the diagnosis of vertical root fractures on CBCT images of endodontically treated and also vital teeth. With the aid of the model, the use of CBCT images is more effective than periapical radiographs. |
|
Kise [32] (2019) | The deep learning model showed high diagnostic accuracy for SjS, which is comparable to that of experienced radiologists. It is suggested that the model could be used to assist the diagnosis of SjS, especially for inexperienced radiologists. |
|
Kann [31] (2018) | The proposed deep learning model has the potential for use as a clinical decision-making tool to help guide head and neck cancer patient management. |
|
Ariji [20] (2019) | The proposed deep learning model yielded diagnostic results comparable to that of radiologists, which suggests that the model may be valuable for diagnostic support. |
|
Cheng [33] (2011) | The proposed model can efficiently assist clinicians in locating the odontoid process of the second vertebra. |
|
Shahidi [34] (2014) | The localization performance of the proposed model was acceptable with a mean deviation of 3.40 mm for all automatically identified landmarks. |
|
Montufar [21] (2018) | The proposed algorithm for automatically locating landmarks on CBCT volumes seems to be useful for 3D cephalometric analysis. |
|
Montufar [22] (2018) | The proposed hybrid algorithm for automatic landmarking on CBCT volumes seems to be potentially useful for 3D cephalometric analysis. |
|
Torosdagli [35] (2019) | The proposed deep learning algorithm allows for orthodontic analysis in patients with craniofacial deformities exhibiting excellent performance. |
|
Park [36] (2018) | The proposed deep learning algorithm is useful for super-resolution and de-noising. |
|
Minnema [23] (2019) | The proposed deep learning algorithm allows us to accurately classify metal artifacts as background noise, and to segment teeth and bony structures. |
|
Miki [38] (2017) | The proposed deep learning algorithm to classify tooth types on CBCTs yielded a high performance. This can be effectively used for automated preparation of dental charts and might be useful in forensic identification. |
|
Ghazvinian Zanjani [24] (2019) | The proposed end-to-end deep learning framework for the segmentation of individual teeth and the gingiva from intraoral scans outperforms state-of-the-art networks. |
|
Kim [45] (2020) | The proposed automated segmentation method for full arch intraoral scan data is as accurate as a manual segmentation method. This tool could efficiently facilitate the digital setup process in orthodontic treatment. |
|
Lian [25] (2020) | The proposed end-to-end deep neural network to automatically label individual teeth on raw dental surfaces acquired by 3D intraoral scanners outperforms the state-of-the-art methods for 3D shape segmentation. |
|
Liu [27] (2016) | The proposed machine learning algorithm based on face scanning patterns could support current clinical practice of the screening and diagnosis of ASD |
|
Knoops [26] (2019) | The proposed model can automatically analyze facial shape features and provide patient-specific treatment plans from a 3D facial scan. This may benefit the clinical decision-making process and improve clinical understanding of face shape as a marker for plastic and reconstructive surgery. |
|
3D, three-dimensional; AI, artificial intelligence; ASD, autism spectrum disorder; CBCT, cone beam computed tomography; CT, computed tomography; CNN, convolutional neural network; ROI, region of interest; SjS, Sjögren’s syndrome; * risk of bias.