Aplikasi dan pengembangan terkini artificial intelligence untuk analisis implan gigi osseointegrasi berbasis radiografi: Scoping Review

Nabila Haditya Arius, Farina Pramanik, Yurika Ambar Lita

Abstract


ABSTRAK

Pendahuluan: Osseointegrasi merupakan faktor kunci dalam keberhasilan implan gigi, namun evaluasi konvensional seperti radiografi 2D dan histomorfometri memiliki keterbatasan dalam subjektivitas dan efektivitas. Artificial Intelligence (AI) menawarkan solusi inovatif untuk meningkatkan presisi dan kecepatan analisis. Tujuan penelitian ini untuk menganalisis aplikasi dan pengembangan AI dalam analisis osseointegrasi implan gigi menggunakan radiografi melalui scoping review. Metode: Pencarian sistematis dilakukan di PubMed, Scopus, MEDLINE, Embase, dan Web of Science (2014–2024) dengan kerangka PCC (Population: pasien implan gigi, Concept: AI, Context: klinis) menggunakan framework Arksey dan O’Malley serta panduan dari Joanna Briggs Institute (JBI). Hasil: Dari 11 artikel terpilih (2019–2024), mayoritas menggunakan radiografi periapikal dan CBCT sebagai modalitas utama, dengan model deep learning berbasis CNN (Convolution Neural Network) (seperti YOLOv7 dan ResNet-50) menunjukkan kinerja optimal dalam memprediksi kehilangan tulang marginal (akurasi 70,2–96,13%) dan stabilitas implan. Radiografi periapikal unggul dalam akurasi (94,74%) dan presisi (100%), sementara CBCT (Cone Beam Computed Tomography) menawarkan analisis volumetrik lebih detail dengan kecepatan pemrosesan hingga 76 ms. Meski demikian, variasi parameter radiografi dan ketergantungan pada dataset kecil (44–2920 gambar) berpotensi menyebabkan overfitting. Kolaborasi multi-institusi dan standarisasi teknik radiografi diperlukan untuk meningkatkan kemampuan AI dalam praktik klinis. Simpulan: Model deep learning (CNN, YOLOv7) dan machine learning (SVM) terbukti efektif dalam analisis osseointegrasi, terutama untuk marginal bone loss menggunakan radiografi periapikal dan CBCT. AI berpotensi merevolusi evaluasi implan gigi, namun implementasi klinis memerlukan validasi eksternal dan standardisasi data.

KATA KUNCI: artificial intelligence, implan gigi, osseointegrasi, deep learning, analisis radiografi

Current applications and development of artificial intelligence for osseointegration dental implant analysis: Scoping Review

ABSTRACT

Introduction: Osseointegration is a key factor in dental implant success, but conventional evaluations such as two dimensional (2D) radiography and histomorphometry are limited by subjectivity and restricted diagnostic capacity. Artificial Intelligence (AI) offers an innovative solution to improve both precision and speed of analysis. This study aims to explore current applications and advancements in AI-based osseointegration analysis of dental implants using radiographs through a scoping review. Methods: A systematic search was conducted in PubMed, Scopus, MEDLINE, Embase, and Web of Science (2014–2024), using the PCC framework (Population: dental implant patients, Concept: AI, Context: clinical). The review followed the Arksey and O’Malley methodological framework and the Joanna Briggs Institute (JBI) guidelines. Results: Of the 11 selected articles (2019-2024), the majority used periapical radiography and CBCT (Cone Beam Computed Tomography) as the primary imaging modalities, with CNN (Convolution Neural Network)-based deep learning models (such as YOLOv7 and ResNet-50) demonstrated strong predictive performance for marginal bone loss (accuracy 70.2-96.13%) and implant stability. Periapical radiographs achieved high accuracy (94.74%) and precision (100%), while CBCT enabled more detailed volumetric analysis with processing speeds of up to 76 ms. However, variability in radiographic parameters and reliance on small datasets (44-2920 images) could lead to model overfitting. Multi-institutional collaboration and standardization of imaging protocols are required to enhance AI performance and generalizability in clinical practice. Conclusion: Deep learning (CNN, YOLOv7) and machine learning (SVM) models have proven effective in osseointegration analysis, particularly in predicting marginal bone loss using periapical radiographs and CBCT. AI has the potential to revolutionize dental implant evaluation, but clinical implementation requires external validation and data standardization.

KEY WORDS: artificial intelligence, dental implant, osseointegration, deep learning, radiographic analysis



Keywords


Artificial intelligence, implan gigi, osseointegrasi, deep learning, analisis radiografi, Artificial intelligence, dental implant, osseointegration, deep learning, radiographic analysis

Full Text:

PDF

References


DAFTAR PUSTAKA

Brånemark PI, Breine U, Adell R, Hansson BO, Lindström J, Ohlsson A. Intra-osseous anchorage of dental prostheses. I. Experimental studies. Scand J PlOsseointegration of Dental Implants in Patients with Congenital and Degenerative Bone Disorders: A Literature Review. J Int Soc Prevent Community Dentistry. 2023;13(3):167–172. https://doi.org/10.4103/jispcd.JISPCD_51_22

Smeets R, Stadlinger B, Schwarz F, Beck-Broichsitter B, Jung O, Precht C, Kloss F, Gröbe A, Heiland M, Ebker T. Impact of Dental Implant Surface Modifications on Osseointegration. Biomed Res Int. 2016;2016:6285620. https://doi.org/10.1155/2016/6285620

Ho K, Bahammam S, Chen CY, Hojo Y, Kim D, Kondo H, Da Silva J, Nagai S. A cross-sectional survey of patient’s perception and knowledge of dental implants in Japan. Int J Implant Dent. 2022;8(1):14. https://doi.org/10.1186/s40729-022-00410-w

Elani HW, Starr JR, Da Silva JD, Gallucci GO. Trends in Dental Implant Use in the U.S., 1999–2016, and Projections to 2026. J Dent Res [Internet]. 2018 Dec 1 [cited 2024 Sep 11];97(13):1424. Available from: /pmc/articles/PMC6854267/

Dioguardi M, Spirito F, Quarta C, Sovereto D, Basile E, Ballini A, et al. Guided Dental Implant Surgery: Systematic Review. J Clin Med [Internet]. 2023 Feb 1 [cited 2024 Sep 11];12(4):1490. Available from: /pmc/articles/PMC9967359/

Thiebot N, Hamdani A, Blanchet F, Dame M, Tawfik S, Mbapou E, et al. Implant failure rate and the prevalence of associated risk factors: a 6-year retrospective observational survey. Journal of Oral Medicine and Oral Surgery [Internet]. 2022 [cited 2024 Sep 11];28(2):19. Available from: https://www.jomos.org/articles/mbcb/full_html/2022/02/mbcb210065/mbcb210065.html

Frumkin N, Iden JA, Schwartz-Arad D. Effect of osteopenia and osteoporosis on failure of first and second dental implants: a retrospective observational study. International Journal of Implant Dentistry 2024 10:1 [Internet]. 2024 Sep 4 [cited 2024 Oct 7];10(1):1–8. Available from: https://journalimplantdent.springeropen.com/articles/10.1186/s40729-024-00556-9

Stocchero M, Jinno Y, Toia M, Ahmad M, Papia E, Yamaguchi S, et al. Intraosseous Temperature Change during Installation of Dental Implants with Two Different Surfaces and Different Drilling Protocols: An In Vivo Study in Sheep. J Clin Med [Internet]. 2019 Aug 1 [cited 2024 Oct 7];8(8). Available from: /pmc/articles/PMC6723378/

Shang Y, Gao Q, Lengas T, Deng S. Postsurgical Pain and Implant Osseointegration Failure: A Case Control Study. Int J Dent [Internet]. 2022 [cited 2024 Sep 18];2022. Available from: /pmc/articles/PMC9283066 /

Gupta A, Kale B, Masurkar D, Jaiswal P, Gupta A, Kale B, et al. Etiology of dental implant complication and failure—an overview. AIMS Bioengineering 2023 2:141 [Internet]. 2023 [cited 2024 Sep 18];10(2):141–52. Available from: http://www.aimspress.com/article/doi/10.3934/bioeng.2023010

Kastala V. Methods to measure implant stability. Journal of Dental Implants [Internet]. 2018 [cited 2024 Sep 18];8(1):3. Available from: https://journals.lww.com/jodi/fulltext/2018/08010/methods_to_measure_implant_stability.2.aspx

Soylu E, Coşgunarslan A, Çelebi S, Soydan D, Demirbaş AE, Demir O. Fractal analysis as a useful predictor for determining osseointegration of dental implant? A retrospective study. Int J Implant Dent [Internet]. 2021 Dec [cited 2024 Sep 12];7(1). Available from: /pmc/articles/PMC7904985/

Sanjay D, Lagdive B, Shah RJ, Himanshu D, Vadher M. Success Criteria For Dental implant – A Literature Review. Int J Recent Sci Res [Internet]. 2019;10(05(A)):30693–6. Available from: http://dx.doi.org/10.24327/ijrsr.2019.1005.3105

Neto JDP, Melo G, Marin C, Rivero ERC, Cruz ACC, Flores-Mir C, et al. Diagnostic performance of periapical and panoramic radiography and cone beam computed tomography for detection of circumferential gaps simulating osseointegration failure around dental implants: A systematic review. Oral Surg Oral Med Oral Pathol Oral Radiol [Internet]. 2021 Dec 1 [cited 2025 Jan 19];132(6):e208–22. Available from: https://pubmed.ncbi.nlm.nih.gov/34580020/

Kormas I, Pedercini C, Pedercini A, Raptopoulos M, Alassy H, Wolff LF. Peri-Implant Diseases: Diagnosis, Clinical, Histological, Microbiological Characteristics and Treatment Strategies. A Narrative Review. Antibiotics [Internet]. 2020 Nov 1 [cited 2024 Sep 11];9(11):1–19. Available from: /pmc/articles/PMC7700146/

Lubis RT, Azhari A, Pramanik F. Analysis of Bone Density and Bone Morphometry by Periapical Radiographs in Dental Implant Osseointegration Process. Int J Dent [Internet]. 2023 [cited 2024 Sep 11];2023. Available from: /pmc/articles/PMC10085658/

He T, Cao C, Xu Z, Li G, Cao H, Liu X, et al. A comparison of micro-CT and histomorphometry for evaluation of osseointegration of PEO-coated titanium implants in a rat model. Sci Rep [Internet]. 2017 Dec 1 [cited 2024 Sep 18];7(1). Available from: /pmc/articles/PMC5701240/

Pikner SS. Radiographic follow-up analysis of Brånemark dental implants. Swed Dent J Suppl. 2018;(194):5–69.

Zanetti EM, Pascoletti G, Calì M, Bignardi C, Franceschini G. Clinical Assessment of Dental Implant Stability During Follow-Up: What Is Actually Measured, and Perspectives. Biosensors (Basel) [Internet]. 2018 Jul 13 [cited 2024 Sep 18];8(3). Available from: /pmc/articles/PMC6165397/

Putra RH, Doi C, Yoda N, Astuti ER, Sasaki K. Current applications and development of artificial intelligence for digital dental radiography. Dentomaxillofacial Radiology. 2022;51(1).

Alshenaiber R, Alharbi R, Alzahrani B, AlShehri K, Alnafisah F, Aljulayfi I, et al. A Prediction of Tooth Restorability Using Artificial Intelligence versus Natural Intelligence: Preliminary Study. 2024 Jun 26 [cited 2025 Feb 17]; Available from: https://www.researchsquare.com

Meng HW, Chien EY, Chien HH. Immediate Implant Placement and Provisionalization in the Esthetic Zone: A 6.5-Year Follow-Up and Literature Review. Case Rep Dent [Internet]. 2021 [cited 2025 Mar 21];2021:4290193. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC8457954/

Putra RH, Doi C, Yoda N, Astuti ER, Sasaki K. Current applications and development of artificial intelligence for digital dental radiography. Dentomaxillofacial Radiology [Internet]. 2022 Jan 1 [cited 2024 Sep 11];51(1):51. Available from: /pmc/articles/PMC8693331/

Li S, Deng YQ, Zhu ZL, Hua HL, Tao ZZ. A Comprehensive Review on Radiomics and Deep Learning for Nasopharyngeal Carcinoma Imaging. Diagnostics 2021, Vol 11, Page 1523 [Internet]. 2021 Aug 24 [cited 2024 Sep 12];11(9):1523. Available from: https://www.mdpi.com/2075-4418/11/9/1523/htm

Talaei Khoei T, Ould Slimane H, Kaabouch N. Deep learning: systematic review, models, challenges, and research directions. Neural Comput Appl [Internet]. 2023 Nov 1 [cited 2024 Sep 28];35(31):23103–24. Available from: https://link.springer.com/article/10.1007/s00521-023-08957-4

Lee JH, Kim DH, Jeong SN, Choi SH. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent. 2018 Oct 1;77:106–11.

Kühnisch J, Meyer O, Hesenius M, Hickel R, Gruhn V. Caries Detection on Intraoral Images Using Artificial Intelligence. J Dent Res. 2022 ;101(2):158.

Hartman H, Nurdin D, Akbar S, Cahyanto A, Setiawan AS. Exploring the potential of artificial intelligence in paediatric dentistry: A systematic review on deep learning algorithms for dental anomaly detection. Int J Paediatr Dent [Internet]. 2024 Sep 1 [cited 2024 Sep 27];34(5):639–52. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/ipd.13164

Satapathy SK, Kunam A, Rashme R, Sudarsanam PP, Gupta A, Kiran Kumar HS. AI-Assisted Treatment Planning for Dental Implant Placement: Clinical vs AI-Generated Plans. J Pharm Bioallied Sci [Internet]. 2024 Feb 1 [cited 2024 Sep 27];16(Suppl 1):S939. Available from: /pmc/articles/PMC11001018/

Kwak GH, Kwak EJ, Song JM, Park HR, Jung YH, Cho BH, et al. Automatic mandibular canal detection using a deep convolutional neural network. Scientific Reports 2020 10:1 [Internet]. 2020 Mar 31 [cited 2024 Sep 27];10(1):1–8. Available from: https://www.nature.com/articles/s41598-020-62586-8

Oh S, Kim YJ, Kim J, Jung JH, Lim HJ, Kim BC, et al. Deep learning-based prediction of osseointegration for dental implant using plain radiography. BMC Oral Health [Internet]. 2023 Dec 1 [cited 2024 Sep 11];23(1):1–7. Available from: https://bmcoralhealth.biomedcentral.com/articles/10.1186/s12903-023-02921-3

Cha JY, Yoon HI, Yeo IS, Huh KH, Han JS. Peri-Implant Bone Loss Measurement Using a Region-Based Convolutional Neural Network on Dental Periapical Radiographs. J Clin Med [Internet]. 2021 Mar 1 [cited 2024 Sep 27];10(5):1–12. Available from: /pmc/articles/PMC7958615/

Huang N, Liu P, Yan Y, Xu L, Huang Y, Fu G, et al. Predicting the risk of dental implant loss using deep learning. J Clin Periodontol [Internet]. 2022 Sep 1 [cited 2024 Sep 27];49(9):872–83. Available from: https://onlinelibrary.wiley.com/doi/full/10.1111/jcpe.13689

Elgarba BM, Fontenele RC, Tarce M, Jacobs R. Artificial intelligence serving pre-surgical digital implant planning: A scoping review. J Dent. 2024 Apr 1;143:104862.

Wu Z, Yu X, Wang F, Xu C. Application of artificial intelligence in dental implant prognosis: A scoping review. J Dent [Internet]. 2024 May 1 [cited 2025 Feb 17];144. Available from: https://pubmed.ncbi.nlm.nih.gov/38467177/

Westphaln KK, Regoeczi W, Masotya M, Vazquez-Westphaln B, Lounsbury K, McDavid L, Lee H, Johnson J, Ronis SD. From Arksey and O’Malley and Beyond: Customizations to enhance a team-based, mixed approach to scoping review methodology. MethodsX; 2021 [cited 2024 Oct 9];8:101375. https://doi.org/10.1016/j.mex.2021.101375

Hadie SNH. ABC of a scoping review: a simplified JBI scoping review guideline. Educ Med J. 2024;16(2):185–197. https://doi.org/10.21315/eimj2024.16.2.14

Liu M, Wang S, Chen H, Liu Y. A pilot study of a deep learning approach to detect marginal bone loss around implants. BMC Oral Health. 2022 Dec 1;22(1).

Vera M, Gómez-Silva MJ, Vera V, López-González CI, Aliaga I, Gascó E, et al. Artificial Intelligence Techniques for Automatic Detection of Peri-implant Marginal Bone Remodeling in Intraoral Radiographs. J Digit Imaging. 2023 Oct 1;36(5):2259–77.

Troiano G, Fanelli F, Rapani A, Zotti M, Lombardi T, Zhurakivska K, et al. Can radiomic features extracted from intra-oral radiographs predict physiological bone remodelling around dental implants? A hypothesis-generating study. J Clin Periodontol. 2023 Jul 1;50(7):932–41.

Oh S, Kim YJ, Kim J, Jung JH, Lim HJ, Kim BC, et al. Deep learning-based prediction of osseointegration for dental implant using plain radiography. BMC Oral Health. 2023 Dec 1;23(1).

Zhang C, Fan L, Zhang S, Zhao J, Gu Y. Deep learning based dental implant failure prediction from periapical and panoramic films. Quant Imaging Med Surg. 2023 Feb 1;13(2):935–45.

Lee WF, Day MY, Fang CY, Nataraj V, Wen SC, Chang WJ, et al. Establishing a novel deep learning model for detecting peri-implantitis. J Dent Sci [Internet]. 2024 Apr 1 [cited 2024 Nov 16];19(2):1165–73. Available from: http://www.ncbi.nlm.nih.gov/pubmed/38618118

Cha JY, Yoon HI, Yeo IS, Huh KH, Han JS. Peri-Implant Bone Loss Measurement Using a Region-Based Convolutional Neural Network on Dental Periapical Radiographs. J Clin Med [Internet]. 2021 Mar 2 [cited 2024 Nov 16];10(5):1–12. Available from: http://www.ncbi.nlm.nih.gov/pubmed/33801384

Sorkhabi MM, Khajeh MS. Classification of alveolar bone density using 3-D deep convolutional neural network in the cone-beam CT images: A 6-month clinical study. MEASUREMENT. 2019;148.

Zhang H, Shan J, Zhang P, Chen X, Jiang H. Trabeculae microstructure parameters serve as effective predictors for marginal bone loss of dental implant in the mandible. Sci Rep. 2020 Dec 1;10(1).

Rajan RSS, Kumar KHS, Sekhar A, Nadakkavukaran D, Feroz SMA, Gangadharappa P. Evaluating the Role of AI in Predicting the Success of Dental Implants Based on Preoperative CBCT Images: A Randomized Controlled Trial. J Pharm Bioallied Sci [Internet]. 2024 Feb 1 [cited 2025 Mar 20];16(Suppl 1):S889–91. Available from: https://pubmed.ncbi.nlm.nih.gov/38595393/

Huang Z, Zheng H, Huang J, Yang Y, Wu Y, Ge L, et al. The Construction and Evaluation of a Multi-Task Convolutional Neural Network for a Cone-Beam Computed-Tomography-Based Assessment of Implant Stability. Diagnostics (Basel) [Internet]. 2022 Nov 1 [cited 2025 Mar 20];12(11). Available from: https://pubmed.ncbi.nlm.nih.gov/36359516/

Shujaat S, Riaz M, Jacobs R. Synergy between artificial intelligence and precision medicine for computer-assisted oral and maxillofacial surgical planning. Clin Oral Investig [Internet]. 2023 Mar 1 [cited 2025 Jan 19];27(3):897–906. Available from: https://pubmed.ncbi.nlm.nih.gov/36323803/

Revilla-León M, Gómez-Polo M, Vyas S, Barmak BA, Galluci GO, Att W, et al. Artificial intelligence applications in implant dentistry: A systematic review. Journal of Prosthetic Dentistry [Internet]. 2023 Feb 1 [cited 2024 Sep 12];129(2):293–300. Available from: http://www.thejpd.org/article/S0022391321002729/fulltext

Thurzo A, Urbanová W, Novák B, Czako L, Siebert T, Stano P, et al. Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis. Healthcare (Basel) [Internet]. 2022 Jul 1 [cited 2025 Feb 14];10(7). Available from: https://pubmed.ncbi.nlm.nih.gov/35885796/

Elsonbaty S, Elgarba BM, Fontenele RC, Swaity A, Jacobs R. Novel AI-based tool for primary tooth segmentation on CBCT using convolutional neural networks: A validation study. Int J Paediatr Dent [Internet]. 2025 Jan 1 [cited 2025 Feb 14];35(1). Available from: https://pubmed.ncbi.nlm.nih.gov/38769619/

Pinotti FE, Aroni MAT, de Oliveira GJPL, Silva BLG, Marcantonio Junior E, Marcantonio RAC. Osseointegration of implants with superhydrophilic surfaces in rats with high serum levels of nicotine. Braz Dent J [Internet]. 2023 [cited 2025 Feb 14];34(2):105–12. Available from: https://pubmed.ncbi.nlm.nih.gov/37194848/

Park JH, Moon HS, Jung HI, Hwang JJ, Choi YH, Kim JE. Deep learning and clustering approaches for dental implant size classification based on periapical radiographs. Scientific Reports 2023 13:1 [Internet]. 2023 Oct 6 [cited 2024 Oct 4];13(1):1–11. Available from: https://www.nature.com/articles/s41598-023-42385-7

Khatib Sulaiman Dalam No J, Salah Nooraldaim A, Ali Saed A. Artificial Intelligence for Caries and Tooth Detection in Dental Imaging: A Review. The Indonesian Journal of Computer Science [Internet]. 2023 Dec 30 [cited 2025 Feb 14];12(6):2023–3201. Available from: http://ijcs.net/ijcs/index.php/ijcs/article/view/3522

Gao T, Wang G. Chest X-ray image analysis and classification for COVID-19 pneumonia detection using Deep CNN. medRxiv [Internet]. 2020 Oct 12 [cited 2025 Feb 14];2020.08.20.20178913. Available from: https://www.medrxiv.org/content/10.1101/2020.08.20.20178913v2

Nakagawa J, Fujima N, Hirata K, Tang M, Tsuneta S, Suzuki J, et al. Utility of the deep learning technique for the diagnosis of orbital invasion on CT in patients with a nasal or sinonasal tumor. Cancer Imaging [Internet]. 2022 Dec 1 [cited 2025 Feb 14];22(1). Available from: https://pubmed.ncbi.nlm.nih.gov/36138422/

Liu S, Wang Y, Yu Q, Liu H, Peng Z. CEAM-YOLOv7: Improved YOLOv7 Based on Channel Expansion and Attention Mechanism for Driver Distraction Behavior Detection. IEEE Access. 2022;10:129116–24.

Sun Y, Li Y, Li S, Duan Z, Ning H, Zhang Y. PBA-YOLOv7: An Object Detection Method Based on an Improved YOLOv7 Network. Applied Sciences 2023, Vol 13, Page 10436 [Internet]. 2023 Sep 18 [cited 2025 Feb 14];13(18):10436. Available from: https://www.mdpi.com/2076-3417/13/18/10436/htm

Wang S, Tong X, Cheng Q, Xiao Q, Cui J, Li J, et al. Fully automated deep learning system for osteoporosis screening using chest computed tomography images. Quant Imaging Med Surg [Internet]. 2024 Apr 3 [cited 2025 Feb 14];14(4):2816–27. Available from: https://pubmed.ncbi.nlm.nih.gov/38617137/

Bukhari SUK, Mehtab U, Hussain SS, Syed A, Armaghan SU, Shah SSH. The assessment of Computer Vision Algorithms for the Diagnosis of Prostatic Adenocarcinoma in Surgical Specimens. medRxiv [Internet]. 2020 Jul 17 [cited 2025 Feb 14];2020.07.14.20152116. Available from: https://www.medrxiv.org/content/10.1101/2020.07.14.20152116v1

Glembin M, Obuchowski A, Klaudel B, Rydziński B, Karski R, Syty P, et al. Enhancing Renal Tumor Detection: Leveraging Artificial Neural Networks in Computed Tomography Analysis. Medical Science Monitor [Internet]. 2023 Jun 6 [cited 2025 Feb 14];29:0–0. Available from: https://www.medscimonit.com/abstract/full/idArt/939462

Hsieh ST, Cheng YA. Multimodal feature fusion in deep learning for comprehensive dental condition classification. J Xray Sci Technol [Internet]. 2024 [cited 2025 Feb 14];32(2):303–21. Available from: https://pubmed.ncbi.nlm.nih.gov/38217632/

Kaya E, Gunec HG, Gokyay SS, Kutal S, Gulum S, Ates HF. Proposing a CNN Method for Primary and Permanent Tooth Detection and Enumeration on Pediatric Dental Radiographs. J Clin Pediatr Dent [Internet]. 2022 Sep 13 [cited 2025 Feb 14];46(4):293–8. Available from: https://pubmed.ncbi.nlm.nih.gov/36099226/

Alshammari K, Alshammari R, Alshammari A, Alkhudaydi T. An improved pear disease classification approach using cycle generative adversarial network. Scientific Reports 2024 14:1 [Internet]. 2024 Mar 20 [cited 2025 Feb 14];14(1):1–11. Available from: https://www.nature.com/articles/s41598-024-57143-6

Shorten C, Khoshgoftaar TM. A survey on Image Data Augmentation for Deep Learning. J Big Data [Internet]. 2019 Dec 1 [cited 2025 Feb 14];6(1):1–48. Available from: https://journalofbigdata.springeropen.com/articles/10.1186/s40537-019-0197-0

Garcea F, Serra A, Lamberti F, Morra L. Data augmentation for medical imaging: A systematic literature review. Comput Biol Med. 2023 Jan 1;152:106391.

Yates LA, Aandahl Z, Richards SA, Brook BW. Cross validation for model selection: A review with examples from ecology. Ecol Monogr [Internet]. 2023 Feb 1 [cited 2025 Feb 14];93(1):e1557. Available from: https://onlinelibrary.wiley.com/doi/full/10.1002/ecm.1557




DOI: https://doi.org/10.24198/pjdrs.v9i3.64986

DOI (PDF): https://doi.org/10.24198/pjdrs.v9i3.64986.g26792

Refbacks

  • There are currently no refbacks.


 

Creative Commons License All publications by the Universitas Padjadjaran [e-ISSN: 2656-985X], are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License .

Visitor Stat