About

Jin U. Kang is the Jacob Suter Jammer Professor of electrical and computer engineering at Johns Hopkins University. He is an expert in optical imaging, sensing, fiber optic devices, and photonic systems. Kang holds a joint appointment in the Department of Dermatology at the Johns Hopkins University School of Medicine and is a member of the Kavli Neuroscience Discovery Institute and the Laboratory for Computational Sensing and Robotics. His research focuses on developing novel optical techniques and devices for biomedical applications, including sensing and imaging systems, fiber optic sensors and devices such as fiber lasers, and nonlinear and quantum optical efforts. Kang has pioneered the development of endoscopic, common-path fiber optic optical coherence tomography (OCT) techniques for medical imaging and sensing, enabling the creation of microsurgical and robotic tools that facilitate safer and more precise surgical outcomes. His group was the first to implement and demonstrate real-time, 4-D OCT systems that allow surgeons to monitor surgical sites in 3-D video during procedures. He is also working on creating 'smart' tool systems to assist surgeons in microsurgeries, such as retinal surgeries, cornea transplants, vascular surgeries, and cochlear implants. Recently, Kang launched LIV (Live Imaging Vision) Med Tech Inc., a startup to develop image-guided robotic tools. His projects include building real-time Doppler 3-D imaging systems for intraoperative assessment of surgeries like carotid endarterectomy and cerebrovascular surgery. Kang holds more than 20 patents and has received numerous honors, including being a fellow of the Optical Society of America, the International Optics Society (SPIE), and the American Institute for Medical and Biological Engineering. He has received awards such as the ONR Young Investigator Award, the Australian Institute of Advanced Studies Fellowship, a NASA Faculty Fellowship, and others. Kang has served as a topical editor of Optics Letters and is on the editorial boards of the Journal of the Optical Society of Korea and Chinese Optics Letters. His educational background includes a BS in physics from Western Washington University, and a master’s and PhD in optical science and electrical engineering from the University of Central Florida. Prior to joining Johns Hopkins, he worked as a research engineer at the U.S. Naval Research Laboratory.

Research topics

• Artificial Intelligence
• Computer Science
• Computer vision
• Physics
• Medicine
• Telecommunications
• Acoustics
• Mathematics
• Psychology
• Neuroscience
• Internal medicine
• Chemistry

Selected publications

• A Si‐Containing Organophosphinic Acid Salt Endowing Epoxy Resin With Excellent Flame Retardancy, Smoke Suppression, and Toughness

  Journal of Polymer Science · 2025-09-30

  article

  ABSTRACT Epoxy resin (EP) exhibits remarkable mechanical properties and notable chemical stability, making it suitable for widespread industrial applications. However, its inherent flammability limits its use in high‐safety environments. To address this limitation, a novel organophosphate flame retardant (DSZ) containing vinylsilazane flexible chains was synthesized for EP modification. The incorporation of 3 wt% DSZ enabled the flame‐retardant EP (FREP) to achieve a vertical combustion (UL‐94) V‐0 rating with a limiting oxygen index (LOI) of 32.4%, confirming high flame‐retardant efficiency. Cone calorimetry tests (CCT) demonstrated a 32.4% decrease in total smoke production for the 5 wt% DSZ formulation, which can be attributed to the synergistic effects of P–N–Si, which also enhanced smoke suppression. These improvements demonstrate the effectiveness of DSZ in elevating the fire safety of EP. Furthermore, the FREP exhibited a 78% increase in impact strength while its flexural and tensile properties remained unchanged. Collectively, DSZ enables simultaneous flame retardancy, smoke suppression, and mechanical reinforcement, significantly expanding the application scope of EP.

  PublisherDOI

• Development of an image-guided non-vitrectomy subretinal access approach for trans-scleral cell and gene therapy delivery

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-03-01

  preprintOpen accessSenior author

  Abstract Purpose Regenerative therapies for retinal diseases include cell and gene therapy modalities that are targeted to the subretinal space. Several recent clinical trials have shown that the morbidity of surgical access is the major limitation of safe subretinal space delivery. We aimed to develop an image-guided procedure for minimally invasive subretinal access (MISA) as a platform to deliver therapeutic agents for the treatment of degenerative retinal diseases. Methods We engineered prototypes of a novel common-path swept source optical coherence tomography (CP-SSOCT)-enabled needle, coaxial guide (COG), and subretinal access cannula (SAC). We pilot tested the MISA procedure in ex vivo bovine eyes and in vivo porcine ocular surgery. Results A- and M-mode scan recordings of ex vivo and in vivo animal eye models demonstrated that CP-SSOCT imaging from the scleral side ( ab externo ) was capable of identifying the retinal laminae and the sub-retinal space. We show results from in vivo porcine MISA surgeries (N=4) using the novel CP-SSOCT-enabled sub-retinal injection needle, COG, and SAC through the transscleral approach. The MISA approach enabled subretinal device placement in the posterior pole, however, cases of retinal incarceration and retinal perforation were encountered. Conclusions We describe a novel CP-SSOCT-guided subretinal access approach that, with further optimization, may be useful in regenerative retinal surgery.

  PublisherOA PDFDOI

• Kalman filter/deep-learning hybrid automatic boundary tracking of optical coherence tomography data for deep anterior lamellar keratoplasty (DALK)

  2025-03-20

  articleSenior author
  PublisherDOI

• Deep-learning-based endoscopic single-shot fringe projection profilometry

  Journal of Biomedical Optics · 2025-08-19

  articleOpen accessSenior author

  Significance: Conventional fringe projection profilometry (FPP) requires multiple image acquisitions and therefore long acquisition times that make it slow for high-speed dynamic measurements. We propose and demonstrate a deep-learning-based single-shot FPP system utilizing a single endoscope for surgical guidance. Aim: We aim to achieve real-time depth map generation of target tissues with high accuracy for robotic surgical guidance. Approach: We proposed an endoscopic single-shot FPP system based on a deep learning network to generate real-time accurate tissue depth maps for surgical guidance. The system utilizes a dual-channel endoscope, where one channel projects fringe patterns from a projector and the other channel collects images using a camera. In addition, we developed a data synthesis method to generate a large number of diverse training datasets. The network consists of MaskNet, which segments the tissue from the background, and DepthNet, which predicts the depth map of the image. The results from both networks are combined to generate the final depth map. Results: and a processing time of about 12.75 ms per frame. Conclusion: A deep-learning-based single-shot FPP endoscopic system was shown to be highly effective in real-time depth map generation with millimeter-scale error. Implementing such a system has the potential to improve the reliability of image-guided robotic surgery.

  PublisherOA PDFDOI

• Topology-based deep-learning segmentation method for deep anterior lamellar keratoplasty (DALK) surgical guidance using M-mode OCT data

  2025-03-20

  articleSenior author

  Deep Anterior Lamellar Keratoplasty (DALK) is a partial-thickness corneal transplant procedure for treating corneal stromal diseases. A key step involves the precise separation of the deep stroma from Descemet’s membrane (DM) using the Big Bubble technique. To simplify the tasks of needle insertion and pneumo-dissection in this technique, we previously proposed an Optical Coherence Tomography (OCT)-guided, eye-mountable robot that utilizes real-time corneal layers tracking from M-mode OCT signals for control. However, signal noise and instability during the manipulation of the OCT fiber sensor-integrated needle hinder the performance of conventional deep-learning segmentation methods, leading to rough and inaccurate detection of corneal layers. To overcome these limitations, we develop a topology-based deep-learning segmentation method that integrates a topological loss function with a modified network architecture. This approach effectively mitigates noise effects and enhances segmentation precision, stability, and speed. Validation on in vivo, ex vivo, and hybrid rabbit eyes data sets demonstrates that our approach surpasses traditional loss-based techniques, delivering accurate, robust, and fast segmentation of the epithelium and DM for surgical guidance.

  PublisherDOI

• Kalman filter/deep-learning hybrid automatic boundary tracking of optical coherence tomography data for deep anterior lamellar keratoplasty (DALK)

  ArXiv.org · 2025-01-25

  preprintOpen accessSenior author

  Deep anterior lamellar keratoplasty (DALK) is a highly challenging partial thickness cornea transplant surgery that replaces the anterior cornea above Descemet's membrane (DM) with a donor cornea. In our previous work, we proposed the design of an optical coherence tomography (OCT) sensor integrated needle to acquire real-time M-mode images to provide depth feedback during OCT-guided needle insertion during Big Bubble DALK procedures. Machine learning and deep learning techniques were applied to M-mode images to automatically identify the DM in OCT M-scan data. However, such segmentation methods often produce inconsistent or jagged segmentation of the DM which reduces the model accuracy. Here we present a Kalman filter based OCT M-scan boundary tracking algorithm in addition to AI-based precise needle guidance to improve automatic DM segmentation for OCT-guided DALK procedures. By using the Kalman filter, the proposed method generates a smoother layer segmentation result from OCT M-mode images for more accurate tracking of the DM layer and epithelium. Initial ex vivo testing demonstrates that the proposed approach significantly increases the segmentation accuracy compared to conventional methods without the Kalman filter. Our proposed model can provide more consistent and precise depth sensing results, which has great potential to improve surgical safety and ultimately contributes to better patient outcomes.

  PublisherOA PDFDOI

• Design and Evaluation of an Eye Mountable AutoDALK Robot for Deep Anterior Lamellar Keratoplasty

  Micromachines · 2024-06-15 · 3 citations

  articleOpen accessSenior author

  Partial-thickness corneal transplants using a deep anterior lamellar keratoplasty (DALK) approach has demonstrated better patient outcomes than a full-thickness cornea transplant. However, despite better clinical outcomes from the DALK procedure, adoption of the technique has been limited because the accurate insertion of the needle into the deep stroma remains technically challenging. In this work, we present a novel hands-free eye mountable robot for automatic needle placement in the cornea, AutoDALK, that has the potential to simplify this critical step in the DALK procedure. The system integrates dual light-weight linear piezo motors, an OCT A-scan distance sensor, and a vacuum trephine-inspired design to enable the safe, consistent, and controllable insertion of a needle into the cornea for the pneumodissection of the anterior cornea from the deep posterior cornea and Descemet's membrane. AutoDALK was designed with feedback from expert corneal surgeons and performance was evaluated by finite element analysis simulation, benchtop testing, and ex vivo experiments to demonstrate the feasibility of the system for clinical applications. The mean open-loop positional deviation was 9.39 µm, while the system repeatability and accuracy were 39.48 µm and 43.18 µm, respectively. The maximum combined thrust of the system was found to be 1.72 N, which exceeds the clinical penetration force of the cornea. In a head-to-head ex vivo comparison against an expert surgeon using a freehand approach, AutoDALK achieved more consistent needle depth, which resulted in fewer perforations of Descemet's membrane and significantly deeper pneumodissection of the stromal tissue. The results of this study indicate that robotic needle insertion has the potential to simplify the most challenging task of the DALK procedure, enable more consistent surgical outcomes for patients, and standardize partial-thickness corneal transplants as the gold standard of care if demonstrated to be more safe and more effective than penetrating keratoplasty.

  PublisherOA PDFDOI

• Autonomous Robotic System with Optical Coherence Tomography Guidance for Vascular Anastomosis

  arXiv (Cornell University) · 2024-10-10 · 1 citations

  preprintOpen access

  Vascular anastomosis, the surgical connection of blood vessels, is essential in procedures such as organ transplants and reconstructive surgeries. The precision required limits accessibility due to the extensive training needed, with manual suturing leading to variable outcomes and revision rates up to 7.9%. Existing robotic systems, while promising, are either fully teleoperated or lack the capabilities necessary for autonomous vascular anastomosis. We present the Micro Smart Tissue Autonomous Robot (micro-STAR), an autonomous robotic system designed to perform vascular anastomosis on small-diameter vessels. The micro-STAR system integrates a novel suturing tool equipped with Optical Coherence Tomography (OCT) fiber-optic sensor and a microcamera, enabling real-time tissue detection and classification. Our system autonomously places sutures and manipulates tissue with minimal human intervention. In an ex vivo study, micro-STAR achieved outcomes competitive with experienced surgeons in terms of leak pressure, lumen reduction, and suture placement variation, completing 90% of sutures without human intervention. This represents the first instance of a robotic system autonomously performing vascular anastomosis on real tissue, offering significant potential for improving surgical precision and expanding access to high-quality care.

  PublisherOA PDFDOI

• Well-designed flame retardants for epoxy resin composites that simultaneously improve heat resistance, mechanical properties and fire safety

  Materials Today Communications · 2024-05-31 · 7 citations

  article
  PublisherDOI

• Towards Autonomous Retinal Microsurgery Using RGB-D Images

  IEEE Robotics and Automation Letters · 2024-02-21 · 16 citations

  articleOpen access

  Retinal surgery is a challenging procedure requiring precise manipulation of the fragile retinal tissue, often at the scale of tens-of-micrometers. Its difficulty has motivated the development of robotic assistance platforms to enable precise motion, and more recently, novel sensors such as microscope integrated optical coherence tomography (OCT) for RGB-D view of the surgical workspace. The combination of these devices opens new possibilities for robotic automation of tasks such as subretinal injection (SI), a procedure that involves precise needle insertion into the retina for targeted drug delivery. Motivated by this opportunity, we develop a framework for autonomous needle navigation during SI. We develop a system which enables the surgeon to specify waypoint goals in the microscope and OCT views, and the system autonomously navigates the needle to the desired subretinal space in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">real-time</i> . Our system integrates OCT and microscope images with convolutional neural networks (CNNs) to automatically segment the surgical tool and retinal tissue boundaries, and model predictive control that generates optimal trajectories that respect kinematic constraints to ensure patient safety. We validate our system by demonstrating 30 successful SI trials on pig eyes. Preliminary comparisons to a human operator in robot-assisted mode highlight the enhanced safety and performance of our system.

  PublisherOA PDFDOI

Recent grants

• Artificial intelligence Optical Coherence Tomography Guided Deep Anterior Lamellar Keratoplasty (AUTO-DALK)

  NIH · $410k · 2021–2025

• NSF/FDA SIR: Fiber-Optic Common-Path Endoscopic Optical Coherence Tomography for Non-Invasive Optical Biopsy

  NSF · $80k · 2007–2008

• Next Generation of Surgical Imaging and Robotics for Supervised Autonomous Soft Tissue Surgery

  NIH · $1.5M · 2016–2020

• OCT-Guided Free-Hand Semi-Automated Microsurgical Tool for Enhanced Retinal Surge

  NIH · $1.6M · 2011–2017

• OCT-Guided Free-Hand Semi-Automated Microsurgical Tool for Enhanced Retinal Surge

  NIH · $359k · 2011–2016

Frequent coauthors

• Yong Huang

  Guangxi Normal University

  48 shared

• Emad M. Boctor

  Johns Hopkins University

  45 shared

• Russell H. Taylor

  31 shared

• Axel Krieger

  Johns Hopkins University

  29 shared

• Peter Gehlbach

  27 shared

• Ilko K. Ilev

  26 shared

• Hanh N. D. Le

  National Institute of Standards and Technology

  25 shared

• Vikram C. Prabhu

  Loyola University Chicago

  25 shared

Awards & honors

• ONR Young Investigator Award
• Australian Institute of Advanced Studies Fellowship
• NASA Faculty Fellowship
• Oak Ridge Institute of Science and Education fellowship
• Brain Korea Distinguished Faculty Fellowship