Jae‐Hong Lee
About
Jae‐Hong Lee is a scholar working on Oral Surgery, Periodontics and Urology.
According to data from OpenAlex, Jae‐Hong Lee has authored 77 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 49 papers in Oral Surgery, 26 papers in Periodontics and 24 papers in Urology. Recurrent topics in Jae‐Hong Lee's work include Dental Implant Techniques and Outcomes (34 papers), Dental Radiography and Imaging (32 papers) and Oral microbiology and periodontitis research (25 papers). Jae‐Hong Lee is often cited by papers focused on Dental Implant Techniques and Outcomes (34 papers), Dental Radiography and Imaging (32 papers) and Oral microbiology and periodontitis research (25 papers). Jae‐Hong Lee collaborates with scholars based in South Korea, Germany and India. Jae‐Hong Lee's co-authors include Seong‐Nyum Jeong, Do‐Hyung Kim, Seong‐Ho Choi, Young‐Taek Kim, Joachim Krois, Falk Schwendicke, Akhilanand Chaurasia, Jung Kyu Choi, Sergio Uribe and Tarry Singh and has published in prestigious journals such as Scientific Reports, Journal of Dental Research and Journal Of Clinical Periodontology.
In The Last Decade
Jae‐Hong Lee
72 papers receiving 2.6k citations
Hit Papers
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late) cites · hero ref
| Name | h | Career | Trend | Papers | Cites | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| Jae‐Hong Lee South Korea | 23 | 2.0k | 628 | 618 | 493 | 364 | 77 | 2.6k | ||
| Seong‐Nyum Jeong South Korea | 19 | 1.5k 0.7× | 480 0.8× | 455 0.7× | 377 0.8× | 278 0.8× | 49 | 2.0k | ||
| Joachim Krois Germany | 33 | 3.2k 1.6× | 1.2k 1.9× | 929 1.5× | 1.2k 2.4× | 712 2.0× | 95 | 4.9k | ||
| Shilpa Bhandi Saudi Arabia | 21 | 1.0k 0.5× | 337 0.5× | 259 0.4× | 302 0.6× | 74 0.2× | 146 | 1.9k | ||
| Akhilanand Chaurasia India | 19 | 942 0.5× | 231 0.4× | 272 0.4× | 324 0.7× | 209 0.6× | 109 | 1.5k | ||
| İbrahim Şevki Bayrakdar Türkiye | 21 | 1.3k 0.6× | 167 0.3× | 419 0.7× | 288 0.6× | 186 0.5× | 134 | 1.7k | ||
| Karim Elhennawy Germany | 19 | 1.0k 0.5× | 435 0.7× | 255 0.4× | 330 0.7× | 156 0.4× | 29 | 1.7k | ||
| André Ferreira Leite Brazil | 22 | 998 0.5× | 109 0.2× | 371 0.6× | 189 0.4× | 136 0.4× | 82 | 1.9k | ||
| Frank Setzer United States | 28 | 2.2k 1.1× | 124 0.2× | 239 0.4× | 85 0.2× | 55 0.2× | 64 | 2.4k | ||
| Hosam Ali Baeshen Saudi Arabia | 15 | 518 0.3× | 233 0.4× | 145 0.2× | 182 0.4× | 63 0.2× | 84 | 1.2k | ||
| Hossein Mohammad‐Rahimi Iran | 19 | 550 0.3× | 72 0.1× | 181 0.3× | 155 0.3× | 246 0.7× | 51 | 1.1k |
Countries citing papers authored by Jae‐Hong Lee
Since
Specialization
Citations
This map shows the geographic impact of Jae‐Hong Lee's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jae‐Hong Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jae‐Hong Lee more than expected).
Fields of papers citing papers by Jae‐Hong Lee
Since
Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences
This network shows the impact of papers produced by Jae‐Hong Lee. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jae‐Hong Lee. The network helps show where Jae‐Hong Lee may publish in the future.
Co-authorship network of co-authors of Jae‐Hong Lee
This figure shows the co-authorship network connecting the top 25 collaborators of Jae‐Hong Lee. A scholar is included among the top collaborators of Jae‐Hong Lee based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jae‐Hong Lee. Jae‐Hong Lee is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
1.
Lee, Jae‐Hong, Yeon‐Tae Kim, & Falk Schwendicke. (2025). Performance of artificial intelligence-based diagnosis and classification of peri-implantitis compared with periodontal surgeon assessment: a pilot study of panoramic radiograph analysis. Journal of Periodontal & Implant Science. 55(6). 436–436.
2.
Lee, Jae‐Hong & Yeon‐Tae Kim. (2025). Modified tunnel technique with and without enamel matrix derivative for deep and narrow gingival recession in the mandibular anterior region: a 3-year longitudinal and retrospective cohort population-based study. Journal of Periodontal & Implant Science. 55(4). 295–295.
3.
Schneider, Lisa, Joachim Krois, M. Büttner, et al.. (2023). Federated vs Local vs Central Deep Learning of Tooth Segmentation on Panoramic Radiographs. Journal of Dentistry. 135. 104556–104556.
4.
Chaurasia, Akhilanand, et al.. (2023). Deep-learning performance in identifying and classifying dental implant systems from dental imaging: a systematic review and meta-analysis. Journal of Periodontal & Implant Science. 54(1). 3–3.
5.
Lee, Jae‐Hong, Yeon‐Tae Kim, & Seong‐Nyum Jeong. (2023). Alveolar ridge preservation of damaged or periodontally compromised extraction sockets with bovine‐ and porcine‐derived block bone substitutes: A retrospective case–control study. Clinical Implant Dentistry and Related Research. 25(6). 1033–1043.
6.
Lee, Jae‐Hong, et al.. (2023). Porcine-derived soft block bone substitutes for the treatment of severe class II furcation-involved mandibular molars: a prospective controlled follow-up study. Journal of Periodontal & Implant Science. 53(6). 406–406.
7.
Choi, Yujin, et al.. (2022). Association of Dietary Behaviors with Poor Sleep Quality and Increased Risk of Obstructive Sleep Apnea in Korean Military Service Members. Nature and Science of Sleep. Volume 14. 1737–1751.
8.
Lee, Jae‐Hong, et al.. (2022). Profilometric, volumetric, and esthetic analysis of guided bone regeneration with L‐shaped collagenated bone substitute and connective tissue graft in the maxillary esthetic zone: A case series with 1‐year observational study. Clinical Implant Dentistry and Related Research. 24(5). 655–663.
9.
Lee, Jae‐Hong, Young‐Taek Kim, Jong‐Bin Lee, & Seong‐Nyum Jeong. (2022). Deep learning improves implant classification by dental professionals: a multi-center evaluation of accuracy and efficiency. Journal of Periodontal & Implant Science. 52(3). 220–220.
10.
Kim, Yeon‐Tae, Seong‐Nyum Jeong, & Jae‐Hong Lee. (2021). Effectiveness of porcine-derived xenograft with enamel matrix derivative for periodontal regenerative treatment of intrabony defects associated with a fixed dental prosthesis: a 2-year follow-up retrospective study. Journal of Periodontal & Implant Science. 51(3). 179–179.
11.
Kim, Do‐Hyung, Seong‐Nyum Jeong, & Jae‐Hong Lee. (2020). Severe periodontitis with tooth loss as a modifiable risk factor for the development of Alzheimer, vascular, and mixed dementia: National Health Insurance Service-National Health Screening Retrospective Cohort 2002–2015. Journal of Periodontal & Implant Science. 50(5). 303–303.
12.
Lee, Jae‐Hong, et al.. (2019). A Deep Learning Based Approach to Recognizing Accompanying Status of Smartphone Users Using Multimodal Data. Journal of Intelligence and Information Systems. 25(1). 163–177.
13.
Kim, Do‐Hyung, Seong‐Nyum Jeong, & Jae‐Hong Lee. (2019). Soft tissue augmentation with volume stable collagen matrix: Two cases report. 43(2). 161–168.
14.
Kim, Yeon‐Tae, Jung Kyu Choi, Do‐Hyung Kim, Seong‐Nyum Jeong, & Jae‐Hong Lee. (2019). Association between health status and tooth loss in Korean adults: longitudinal results from the National Health Insurance Service-Health Examinee Cohort, 2002–2015. Journal of Periodontal & Implant Science. 49(3). 158–158.
15.
Lee, Jae‐Hong, et al.. (2019). Association between diabetes mellitus and active periodontitis in Korean adults from Korea National Health and Nutrition Examination Surveys 2010–2015. 43(3). 202–209.
16.
Lee, Jae‐Hong, Jung Kyu Choi, Seong‐Nyum Jeong, & Seong‐Ho Choi. (2018). Charlson comorbidity index as a predictor of periodontal disease in elderly participants. Journal of Periodontal & Implant Science. 48(2). 92–92.
17.
Lee, Jae‐Hong, Do‐Hyung Kim, Seong‐Nyum Jeong, & Seong‐Ho Choi. (2018). Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm. Journal of Periodontal & Implant Science. 48(2). 114–114.
18.
Lee, Jae‐Hong, et al.. (2018). Treatment of the cemental tear. 42(4). 248–253.
19.
Kim, Yeon‐Tae, et al.. (2017). Marginal bone level changes in association with different vertical implant positions: a 3-year retrospective study. Journal of Periodontal & Implant Science. 47(4). 231–231.
20.
Lee, Jae‐Hong, et al.. (2017). Trends in the incidence of tooth extraction due to periodontal disease: results of a 12-year longitudinal cohort study in South Korea. Journal of Periodontal & Implant Science. 47(5). 264–264.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
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