Hae-In Choi

Prof. Hae-In Choi

orcid

Research Focus & Metrics

Academic Career Length

10Years (2017-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
1 January 2017 in Journal of Dental Anesthesia and Pain Medicine
Verified
Journal Article
Open access
PDF file
JYJae-Seek You
Jae-Seek You
SKSu-Gwan Kim
Su-Gwan Kim
JOJi-Su Oh
Ji-Su Oh
HCHae-In Choi
Hae-In Choi
MJmyeongkwan Jih
myeongkwan Jih
You JS, et al. J Dent Anesth Pain Med. 2017 Sep;17(3):225-229. https://doi.org/10.17245/jdapm.2017.17.3.225

30 July 2025 in Biomedicines
Verified
Journal Article
Open access
PDF file
SJSeo-Rin Jeong
Seo-Rin Jeong
HCHae-In Choi
Hae-In Choi
KYKeon-Il Yang
Keon-Il Yang
JKJin-Soo Kim
Jin-Soo Kim
JRJi-Won Ryu
Ji-Won Ryu
HPHyun-Jeong Park
Hyun-Jeong Park
Background: Tongue squamous cell carcinoma (TSCC) is an aggressive oral malignancy characterized by early submucosal invasion and a high risk of cervical lymph node metastasis. Accurate and timely diagnosis is essential, but it remains challenging when relying solely on conventional imaging and histopathology. This systematic review aimed to evaluate studies applying artificial intelligence (AI) in the diagnostic imaging of TSCC. Methods: This review was conducted under PRISMA 2020 guidelines and included studies from January 2020 to December 2024 that utilized AI in TSCC imaging. A total of 13 studies were included, employing AI models such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and Random Forest (RF). Imaging modalities analyzed included MRI, CT, PET, ultrasound, histopathological whole-slide images (WSI), and endoscopic photographs. Results: Diagnostic performance was generally high, with area under the curve (AUC) values ranging from 0.717 to 0.991, sensitivity from 63.3% to 100%, and specificity from 70.0% to 96.7%. Several models demonstrated superior performance compared to expert clinicians, particularly in delineating tumor margins and estimating the depth of invasion (DOI). However, only one study conducted external validation, and most exhibited moderate risk of bias in patient selection or index test interpretation. Conclusions: AI-based diagnostic tools hold strong potential for enhancing TSCC detection, but future research must address external validation, standardization, and clinical integration to ensure their reliable and widespread adoption.

30 September 2021 in Journal of Oral Medicine and Pain
Verified
Journal Article
Open access
PDF file
SPSang-Yeap Park
Sang-Yeap Park
JYJae-Seek You
Jae-Seek You
Seong Yong MoonSeong Yong Moon
Seong Yong Moon
JOJi-Su Oh
Ji-Su Oh
HCHae-In Choi
Hae-In Choi
GJGyeo-Woon Jung
Gyeo-Woon Jung

30 March 2022 in Journal of Oral Medicine and Pain
Verified
Journal Article
Open access
PDF file
HLHye-Jung Lee
Hye-Jung Lee
Seong Yong MoonSeong Yong Moon
Seong Yong Moon
JOJi-Su Oh
Ji-Su Oh
HCHae-In Choi
Hae-In Choi
SPSang-Yeap Park
Sang-Yeap Park
JYJae-Seek You
Jae-Seek You
Hye-Jung Lee, Seong-Yong Moon, Ji-Su Oh, Hae-In Choi, Sang-Yeap Park, Tae-Eun Kim, Jae-Seek You. JOMP 2022;47:52-61. https://doi.org/10.14476/jomp.2022.47.1.52

30 September 2021 in Journal of Oral Medicine and Pain
Verified
Journal Article
Open access
PDF file
GJGyeo-Woon Jung
Gyeo-Woon Jung
Seong Yong MoonSeong Yong Moon
Seong Yong Moon
JOJi-Su Oh
Ji-Su Oh
HCHae-In Choi
Hae-In Choi
JYJae-Seek You
Jae-Seek You

30 September 2023 in Oral Biology Research
Verified
Journal Article
Open access
PDF file
TKTae-Eun Kim
Tae-Eun Kim
JYJae-Seek You
Jae-Seek You
Seong Yong MoonSeong Yong Moon
Seong Yong Moon
JOJi-Su Oh
Ji-Su Oh
HCHae-In Choi
Hae-In Choi
SKSu-Wan Kim
Su-Wan Kim
Tae-Eun Kim, Jae-Seek You, Seong-Yong Moon, Ji-Su Oh, Hae-In Choi, and Su-Wan Kim. Oral Biol Res 2023;47:95-102. https://doi.org/10.21851/obr.47.03.202309.95