Munetoshi Akazawa

517 total citations
23 papers, 313 citations indexed

About

Munetoshi Akazawa is a scholar working on Obstetrics and Gynecology, Reproductive Medicine and Pediatrics, Perinatology and Child Health. According to data from OpenAlex, Munetoshi Akazawa has authored 23 papers receiving a total of 313 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Obstetrics and Gynecology, 9 papers in Reproductive Medicine and 8 papers in Pediatrics, Perinatology and Child Health. Recurrent topics in Munetoshi Akazawa's work include Ovarian cancer diagnosis and treatment (8 papers), Maternal and fetal healthcare (7 papers) and Radiomics and Machine Learning in Medical Imaging (4 papers). Munetoshi Akazawa is often cited by papers focused on Ovarian cancer diagnosis and treatment (8 papers), Maternal and fetal healthcare (7 papers) and Radiomics and Machine Learning in Medical Imaging (4 papers). Munetoshi Akazawa collaborates with scholars based in Japan and Taiwan. Munetoshi Akazawa's co-authors include Kazunori Hashimoto, Katsuhiko Noda, Makoto Nishida, Weimin Liu, Yihua Wu, Toshiaki Saito, Kazuya Ariyoshi, Ryohei Yokoyama, Masakazu Nishida and Kenichi Taguchi and has published in prestigious journals such as Scientific Reports, Acta Obstetricia Et Gynecologica Scandinavica and European Journal of Obstetrics & Gynecology and Reproductive Biology.

In The Last Decade

Munetoshi Akazawa

20 papers receiving 303 citations

Peers

Munetoshi Akazawa

RNMI

PPCH

Yasuhisa Kurata Japan

Dustin N. Hartzel United States

Komsun Suwannarurk Thailand

Pamela Causa Andrieu United States

Jean Feng United States

M. Lykourinas Greece

Reza Arabi Belaghi Iran

Ashish Kumar Jha India

Rohit Kumar India

Taek Min Kim South Korea

Yasuhisa Kurata Japan

Munetoshi Akazawa

152 ×1.3

RNMI

50 ×0.5

219 ×2.5

161 ×2.3

25 ×0.5

PPCH

Citations per year, relative to Munetoshi Akazawa Munetoshi Akazawa (= 1×) peers Yasuhisa Kurata

Countries citing papers authored by Munetoshi Akazawa

Since Specialization

Citations

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

Fields of papers citing papers by Munetoshi Akazawa

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Munetoshi Akazawa

This figure shows the co-authorship network connecting the top 25 collaborators of Munetoshi Akazawa. A scholar is included among the top collaborators of Munetoshi Akazawa 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 Munetoshi Akazawa. Munetoshi Akazawa 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

Akazawa, Munetoshi & Kazunori Hashimoto. (2024). Prediction of hemorrhage in placenta previa: Radiomics analysis of pelvic MRI images. European Journal of Obstetrics & Gynecology and Reproductive Biology. 299. 37–42. 2 indexed citations

Akazawa, Munetoshi, et al.. (2023). Preliminary Results of Deep Learning Approach for Preoperative Diagnosis of Ovarian Cancer Based on Pelvic MRI Scans. Anticancer Research. 43(8). 3817–3821. 3 indexed citations

Akazawa, Munetoshi, et al.. (2023). Deep learning algorithm for predicting preterm birth in the case of threatened preterm labor admissions using transvaginal ultrasound. Journal of Medical Ultrasonics. 51(2). 323–330. 1 indexed citations

Akazawa, Munetoshi & Kazunori Hashimoto. (2023). A multimodal deep learning model for predicting severe hemorrhage in placenta previa. Scientific Reports. 13(1). 17320–17320. 9 indexed citations

Akazawa, Munetoshi & Kazunori Hashimoto. (2023). Prediction of Ovarian Cancer Survival using Machine Learning: A Population-Based Study. 6(3). 1 indexed citations

Akazawa, Munetoshi, et al.. (2021). Machine learning approach for the prediction of postpartum hemorrhage in vaginal birth. Scientific Reports. 11(1). 22620–22620. 39 indexed citations

Akazawa, Munetoshi, et al.. (2021). Learning Curve of Robotic-assisted Hysterectomy With Pelvic Lymphadenectomy for Early Stage Endometrial Cancer: Analysis of 81 Cases. Anticancer Research. 41(8). 4173–4178. 3 indexed citations

Akazawa, Munetoshi & Kazunori Hashimoto. (2021). Artificial intelligence in gynecologic cancers: Current status and future challenges – A systematic review. Artificial Intelligence in Medicine. 120. 102164–102164. 91 indexed citations

Akazawa, Munetoshi, et al.. (2020). The application of machine learning for predicting recurrence in patients with early-stage endometrial cancer: a pilot study. Obstetrics & Gynecology Science. 64(3). 266–273. 18 indexed citations

10.

Akazawa, Munetoshi & Kazunori Hashimoto. (2020). Artificial Intelligence in Ovarian Cancer Diagnosis. Anticancer Research. 40(8). 4795–4800. 77 indexed citations

11.

Akazawa, Munetoshi, Yihua Wu, & Weimin Liu. (2019). Allergy-like reactions to methylene blue following laparoscopic chromopertubation: A systematic review of the literature. European Journal of Obstetrics & Gynecology and Reproductive Biology. 238. 58–62. 9 indexed citations

12.

Akazawa, Munetoshi, et al.. (2019). Impact of uterine weight on robotic hysterectomy: Analysis of 500 cases in a single institute. International Journal of Medical Robotics and Computer Assisted Surgery. 15(5). e2026–e2026. 8 indexed citations

13.

Akazawa, Munetoshi, et al.. (2018). Comparison of Electrosurgical Devices for Cervical Conization: Novel Monopolar Scalpel (VIO) Versus Ultrasonic Scalpel. Journal of Lower Genital Tract Disease. 23(1). 43–47. 2 indexed citations

14.

Akazawa, Munetoshi, et al.. (2018). Malignant Transformation of Mature Cystic Teratoma. International Journal of Gynecological Cancer. 28(9). 1650–1656. 7 indexed citations

15.

Akazawa, Munetoshi, et al.. (2018). Adjuvant chemotherapy for a primitive neuroectodermal tumor of the uterine corpus: A case report and literature review. Journal of obstetrics and gynaecology research. 44(10). 2008–2015. 6 indexed citations

16.

Akazawa, Munetoshi, et al.. (2018). Management of a Giant Ovarian Tumor More Than 30 kg: A Case Report and Review of the Literature. Journal of Gynecologic Surgery. 34(5). 243–247. 3 indexed citations

17.

Akazawa, Munetoshi & Makoto Nishida. (2017). Thrombolysis with intravenous recombinant tissue plasminogen activator during early postpartum period: a review of the literature. Acta Obstetricia Et Gynecologica Scandinavica. 96(5). 529–535. 13 indexed citations

18.

Akazawa, Munetoshi, et al.. (2016). Transvaginal Endoscopic Diagnosis and Treatment of a Case of Obstructed Hemivagina and Ipsilateral Renal Anomaly Syndrome.. JAPANESE JOURNAL OF GYNECOLOGIC AND OBSTETRIC ENDOSCOPY. 32(1). 303–308.

19.

Akazawa, Munetoshi, et al.. (2015). Hysteroscopic resection of retained products of conception after temporal laparoscopic uterine artery ligation. Gynecology and Minimally Invasive Therapy. 5(2). 81–83.

20.

Yokoyama, Motofumi, et al.. (2013). A case of hysteroscopic treatment for cesarean scar pregnancy following temporal laparoscopic uterine artery ligation. JAPANESE JOURNAL OF GYNECOLOGIC AND OBSTETRIC ENDOSCOPY. 29(2). 520–524.

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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