321 related articles for article (PubMed ID: 34268376)
1. Artificial intelligence in digital cariology: a new tool for the diagnosis of deep caries and pulpitis using convolutional neural networks.
Zheng L; Wang H; Mei L; Chen Q; Zhang Y; Zhang H
Ann Transl Med; 2021 May; 9(9):763. PubMed ID: 34268376
[TBL] [Abstract][Full Text] [Related]
2. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm.
Lee JH; Kim DH; Jeong SN; Choi SH
J Dent; 2018 Oct; 77():106-111. PubMed ID: 30056118
[TBL] [Abstract][Full Text] [Related]
3. Artificial intelligence for caries detection: a novel diagnostic tool using deep learning algorithms.
Liu Y; Xia K; Cen Y; Ying S; Zhao Z
Oral Radiol; 2024 Jul; 40(3):375-384. PubMed ID: 38498223
[TBL] [Abstract][Full Text] [Related]
4. Diagnosis of interproximal caries lesions with deep convolutional neural network in digital bitewing radiographs.
Bayraktar Y; Ayan E
Clin Oral Investig; 2022 Jan; 26(1):623-632. PubMed ID: 34173051
[TBL] [Abstract][Full Text] [Related]
5. Deep learning for caries lesion detection in near-infrared light transillumination images: A pilot study.
Schwendicke F; Elhennawy K; Paris S; Friebertshäuser P; Krois J
J Dent; 2020 Jan; 92():103260. PubMed ID: 31821853
[TBL] [Abstract][Full Text] [Related]
6. Deep Learning-Based Cataract Detection and Grading from Slit-Lamp and Retro-Illumination Photographs: Model Development and Validation Study.
Son KY; Ko J; Kim E; Lee SY; Kim MJ; Han J; Shin E; Chung TY; Lim DH
Ophthalmol Sci; 2022 Jun; 2(2):100147. PubMed ID: 36249697
[TBL] [Abstract][Full Text] [Related]
7. Prediction of pulp exposure risk of carious pulpitis based on deep learning.
Wang L; Wu F; Xiao M; Chen YX; Wu L
Hua Xi Kou Qiang Yi Xue Za Zhi; 2023 Apr; 41(2):218-224. PubMed ID: 37056189
[TBL] [Abstract][Full Text] [Related]
8. Development and Validation of a Convolutional Neural Network Model to Predict a Pathologic Fracture in the Proximal Femur Using Abdomen and Pelvis CT Images of Patients With Advanced Cancer.
Joo MW; Ko T; Kim MS; Lee YS; Shin SH; Chung YG; Lee HK
Clin Orthop Relat Res; 2023 Nov; 481(11):2247-2256. PubMed ID: 37615504
[TBL] [Abstract][Full Text] [Related]
9. Diagnostic performance of augmented intelligence with 2D and 3D total body photography and convolutional neural networks in a high-risk population for melanoma under real-world conditions: A new era of skin cancer screening?
Cerminara SE; Cheng P; Kostner L; Huber S; Kunz M; Maul JT; Böhm JS; Dettwiler CF; Geser A; Jakopović C; Stoffel LM; Peter JK; Levesque M; Navarini AA; Maul LV
Eur J Cancer; 2023 Sep; 190():112954. PubMed ID: 37453242
[TBL] [Abstract][Full Text] [Related]
10. Comparison of Deep Learning Models for Cervical Vertebral Maturation Stage Classification on Lateral Cephalometric Radiographs.
Seo H; Hwang J; Jeong T; Shin J
J Clin Med; 2021 Aug; 10(16):. PubMed ID: 34441887
[TBL] [Abstract][Full Text] [Related]
11. Caries Detection on Intraoral Images Using Artificial Intelligence.
Kühnisch J; Meyer O; Hesenius M; Hickel R; Gruhn V
J Dent Res; 2022 Feb; 101(2):158-165. PubMed ID: 34416824
[TBL] [Abstract][Full Text] [Related]
12. Image-Based Differentiation of Bacterial and Fungal Keratitis Using Deep Convolutional Neural Networks.
Redd TK; Prajna NV; Srinivasan M; Lalitha P; Krishnan T; Rajaraman R; Venugopal A; Acharya N; Seitzman GD; Lietman TM; Keenan JD; Campbell JP; Song X
Ophthalmol Sci; 2022 Jun; 2(2):100119. PubMed ID: 36249698
[TBL] [Abstract][Full Text] [Related]
13. A novel lightweight deep convolutional neural network for early detection of oral cancer.
Jubair F; Al-Karadsheh O; Malamos D; Al Mahdi S; Saad Y; Hassona Y
Oral Dis; 2022 May; 28(4):1123-1130. PubMed ID: 33636041
[TBL] [Abstract][Full Text] [Related]
14. Convolutional neural network-based artificial intelligence for the diagnosis of early esophageal cancer based on endoscopic images: A meta-analysis.
Ma H; Wang L; Chen Y; Tian L
Saudi J Gastroenterol; 2022; 28(5):332-340. PubMed ID: 35848703
[TBL] [Abstract][Full Text] [Related]
15. Automatic caries detection in bitewing radiographs: part I-deep learning.
Kunt L; Kybic J; Nagyová V; Tichý A
Clin Oral Investig; 2023 Dec; 27(12):7463-7471. PubMed ID: 37968358
[TBL] [Abstract][Full Text] [Related]
16. Artificial intelligence for detection of periapical lesions on intraoral radiographs: Comparison between convolutional neural networks and human observers.
Pauwels R; Brasil DM; Yamasaki MC; Jacobs R; Bosmans H; Freitas DQ; Haiter-Neto F
Oral Surg Oral Med Oral Pathol Oral Radiol; 2021 May; 131(5):610-616. PubMed ID: 33653645
[TBL] [Abstract][Full Text] [Related]
17. Comparison of Convolutional Neural Network Models for Determination of Vocal Fold Normality in Laryngoscopic Images.
Cho WK; Choi SH
J Voice; 2022 Sep; 36(5):590-598. PubMed ID: 32873430
[TBL] [Abstract][Full Text] [Related]
18. An ensemble of features based deep learning neural network for reduction of inappropriate atrial fibrillation detection in implantable cardiac monitors.
Sarkar S; Majumder S; Koehler JL; Landman SR
Heart Rhythm O2; 2023 Jan; 4(1):51-58. PubMed ID: 36713039
[TBL] [Abstract][Full Text] [Related]
19. Efficacy of deep convolutional neural network algorithm for the identification and classification of dental implant systems, using panoramic and periapical radiographs: A pilot study.
Lee JH; Jeong SN
Medicine (Baltimore); 2020 Jun; 99(26):e20787. PubMed ID: 32590758
[TBL] [Abstract][Full Text] [Related]
20. Ovarian tumor diagnosis using deep convolutional neural networks and a denoising convolutional autoencoder.
Jung Y; Kim T; Han MR; Kim S; Kim G; Lee S; Choi YJ
Sci Rep; 2022 Oct; 12(1):17024. PubMed ID: 36220853
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
