Motoki Fukuda

2.1k total citations · 1 hit paper
45 papers, 1.6k citations indexed

About

Motoki Fukuda is a scholar working on Oral Surgery, Otorhinolaryngology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Motoki Fukuda has authored 45 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Oral Surgery, 12 papers in Otorhinolaryngology and 12 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Motoki Fukuda's work include Dental Radiography and Imaging (25 papers), Oral and Maxillofacial Pathology (10 papers) and Radiomics and Machine Learning in Medical Imaging (9 papers). Motoki Fukuda is often cited by papers focused on Dental Radiography and Imaging (25 papers), Oral and Maxillofacial Pathology (10 papers) and Radiomics and Machine Learning in Medical Imaging (9 papers). Motoki Fukuda collaborates with scholars based in Japan and South Korea. Motoki Fukuda's co-authors include Eiichiro Ariji, Yoshiko Ariji, Akitoshi Katsumata, Hiroshi Fujita, Yoshitaka Kise, Michihito Nozawa, Chiaki Kuwada, Kazuhiko Nakata, Chisako Muramatsu and Takuma Funakoshi and has published in prestigious journals such as Scientific Reports, Cancers and Dentomaxillofacial Radiology.

In The Last Decade

Motoki Fukuda

40 papers receiving 1.5k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography

2018 215 citations Yoshiko Ariji, Motoki Fukuda et al. Dentomaxillofacial Radiology profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Motoki Fukuda Japan	18	1.2k	469	427	342	226	45	1.6k
Yoshitaka Kise Japan	19	953 0.8×	362 0.8×	385 0.9×	281 0.8×	173 0.8×	52	1.5k
İbrahim Şevki Bayrakdar Türkiye	21	1.3k 1.1×	419 0.9×	269 0.6×	186 0.5×	188 0.8×	134	1.7k
Sang‐Sun Han South Korea	20	940 0.8×	310 0.7×	270 0.6×	133 0.4×	141 0.6×	96	1.4k
Sam-Sun Lee South Korea	22	1.3k 1.1×	431 0.9×	300 0.7×	135 0.4×	71 0.3×	119	2.0k
Jae Joon Hwang South Korea	17	864 0.7×	318 0.7×	189 0.4×	130 0.4×	146 0.6×	58	1.1k
Michihito Nozawa Japan	13	569 0.5×	224 0.5×	277 0.6×	190 0.6×	89 0.4×	26	896
Holger Willems Sweden	14	953 0.8×	452 1.0×	244 0.6×	114 0.3×	133 0.6×	16	1.1k
Yun‐Hoa Jung South Korea	18	927 0.8×	219 0.5×	149 0.3×	93 0.3×	79 0.3×	57	1.2k
Jo‐Eun Kim South Korea	15	694 0.6×	255 0.5×	188 0.4×	109 0.3×	102 0.5×	72	957
Bong‐Hae Cho South Korea	17	905 0.8×	211 0.4×	131 0.3×	80 0.2×	66 0.3×	46	1.2k

Countries citing papers authored by Motoki Fukuda

Since Specialization

Citations

This map shows the geographic impact of Motoki Fukuda'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 Motoki Fukuda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Motoki Fukuda more than expected).

Fields of papers citing papers by Motoki Fukuda

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Motoki Fukuda. 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 Motoki Fukuda. The network helps show where Motoki Fukuda may publish in the future.

Co-authorship network of co-authors of Motoki Fukuda

This figure shows the co-authorship network connecting the top 25 collaborators of Motoki Fukuda. A scholar is included among the top collaborators of Motoki Fukuda 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 Motoki Fukuda. Motoki Fukuda is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Kise, Yoshitaka, et al.. (2025). Preliminary approach to creation of a prediction model for diagnosis of Sjögren's syndrome using radiomics and machine learning techniques on computed tomography images of the parotid glands. Imaging Science in Dentistry. 55(2). 189–189.

Kuwada, Chiaki, Yasunaga MITSUYA, Motoki Fukuda, et al.. (2025). Area detection improves the person-based performance of a deep learning system for classifying the presence of carotid artery calcifications on panoramic radiographs. Oral Radiology. 42(1). 91–100.

Eida, Sato, Motoki Fukuda, Ikuo Katayama, et al.. (2024). Metastatic Lymph Node Detection on Ultrasound Images Using YOLOv7 in Patients with Head and Neck Squamous Cell Carcinoma. Cancers. 16(2). 274–274. 9 indexed citations

Danjo, Atsushi, Chiaki Kuwada, Reona Aijima, et al.. (2024). Limitations of panoramic radiographs in predicting mandibular wisdom tooth extraction and the potential of deep learning models to overcome them. Scientific Reports. 14(1). 30806–30806. 1 indexed citations

Fukuda, Motoki, Michihito Nozawa, Hironori Akiyama, Eiichiro Ariji, & Yoshiko Ariji. (2024). Improved soft-tissue visibility on cone-beam computed tomography with an image-generating artificial intelligence model using a cyclic generative adversarial network. Oral Radiology. 40(4). 508–519. 1 indexed citations

Iwase, Yukiko, Yoshitaka Kise, Michihito Nozawa, et al.. (2024). Deep learning classification performance for diagnosing condylar osteoarthritis in patients with dentofacial deformities using panoramic temporomandibular joint projection images. Oral Radiology. 40(4). 538–545.

Fukuda, Motoki, Michihito Nozawa, Chiaki Kuwada, et al.. (2024). A cycle generative adversarial network for generating synthetic contrast-enhanced computed tomographic images from non-contrast images in the internal jugular lymph node-bearing area. Odontology. 112(4). 1343–1352. 3 indexed citations

Ariji, Yoshiko, et al.. (2024). Two-step deep learning models for detection and identification of the manufacturers and types of dental implants on panoramic radiographs. Odontology. 113(2). 788–798. 5 indexed citations

Nozawa, Michihito, et al.. (2024). Evaluation of temporomandibular joint osteoarthritis using a new FRACTURE sequence of 3.0T MRI. Dentomaxillofacial Radiology. 54(1). 64–69.

10.

Ariji, Yoshiko, Kazuyuki Araki, Motoki Fukuda, et al.. (2023). Effects of the combined use of segmentation or detection models on the deep learning classification performance for cyst‐like lesions of the jaws on panoramic radiographs: Preliminary research. Oral Science International. 21(2). 198–206. 7 indexed citations

11.

Kise, Yoshitaka, et al.. (2023). Deep learning system for distinguishing between nasopalatine duct cysts and radicular cysts arising in the midline region of the anterior maxilla on panoramic radiographs. Imaging Science in Dentistry. 54(1). 33–33. 3 indexed citations

12.

Kuwada, Chiaki, Yoshiko Ariji, Yoshitaka Kise, et al.. (2022). Detection of unilateral and bilateral cleft alveolus on panoramic radiographs using a deep-learning system. Dentomaxillofacial Radiology. 52(8). 20210436–20210436. 9 indexed citations

13.

Ariji, Yoshiko, et al.. (2022). Automatic visualization of the mandibular canal in relation to an impacted mandibular third molar on panoramic radiographs using deep learning segmentation and transfer learning techniques. Oral Surgery Oral Medicine Oral Pathology and Oral Radiology. 134(6). 749–757. 23 indexed citations

14.

Akiyama, Hironori, et al.. (2022). Transfer learning in diagnosis of maxillary sinusitis using panoramic radiography and conventional radiography. Oral Radiology. 39(3). 467–474. 10 indexed citations

15.

Ariji, Yoshiko, Motoki Fukuda, Takuma Funakoshi, et al.. (2021). Performance of deep learning technology for evaluation of positioning quality in periapical radiography of the maxillary canine. Oral Radiology. 38(1). 147–154. 13 indexed citations

16.

Ariji, Yoshiko, Motoki Fukuda, Michihito Nozawa, et al.. (2020). Automatic detection of cervical lymph nodes in patients with oral squamous cell carcinoma using a deep learning technique: a preliminary study. Oral Radiology. 37(2). 290–296. 35 indexed citations

17.

Watanabe, Hirofumi, Yoshiko Ariji, Motoki Fukuda, et al.. (2020). Deep learning object detection of maxillary cyst-like lesions on panoramic radiographs: preliminary study. Oral Radiology. 37(3). 487–493. 52 indexed citations

18.

Ariji, Yoshiko, Motoki Fukuda, Yoshitaka Kise, et al.. (2018). A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography. Dentomaxillofacial Radiology. 48(3). 20180218–20180218. 215 indexed citations breakdown →

19.

Ariji, Yoshiko, Akitoshi Katsumata, Hiroshi Fujita, et al.. (2016). Utilization of computer-aided detection system in diagnosing unilateral maxillary sinusitis on panoramic radiographs. Dentomaxillofacial Radiology. 45(3). 20150419–20150419. 35 indexed citations

20.

Kawano, Sayaka, Naoya Sato, Motoki Fukuda, et al.. (1982). [Effect of cigarette smoking on the gastric hemodynamics.--Analysis by reflectance spectrophotometry (author's transl)].. PubMed. 79(2). 187–92. 7 indexed citations

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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