Quantitative Screening of Cervical Cancers for Low-Resource Settings: Pilot Study of Smartphone-Based Endoscopic Visual Inspection After Acetic Acid Using Machine Learning Techniques

Background: Approximately 90% of global cervical cancer (CC) is mostly found in low- and middle-income countries. In most cases, CC can be detected early through routine screening programs, including a cytology-based test. However, it is logistically difficult to offer this program in low-resource settings due to limited resources and infrastructure, and few trained experts. A visual inspection following the application of acetic acid (VIA) has been widely promoted and is routinely recommended as a viable form of CC screening in resource-constrained countries. Digital images of the cervix have been acquired during VIA procedure with better quality assurance and visualization, leading to higher diagnostic accuracy and reduction of the variability of detection rate. However, a colposcope is bulky, expensive, electricity-dependent, and needs routine maintenance, and to confirm the grade of abnormality through its images, a specialist must be present. Recently, smartphone-based imaging systems have made a significant impact on the practice of medicine by offering a cost-effective, rapid, and noninvasive method of evaluation. Furthermore, computer-aided analyses, including image processing-based methods and machine learning techniques, have also shown great potential for a high impact on medicinal evaluations

Development of deep learning integrated futuristic biomedical platforms for digital healthcare

Department of Biomedical EngineeringThe success of artificial intelligence to harness the complexity of data is becoming more vivid for biomedical use. For the digital transformation of healthcare, its role is essential: evidence-based decisions could become a new norm and understanding of our health could be augmented with the help of the technology integration. Moreover, enhanced efficiency, safety, and access in the delivery of healthcare services could be achieved. Working in symbiosis with human, AI devices should be developed to aid in hard tasks, help interpreting complex patterns behind abnormality processes, which are less likely to be solved with conventional computational methods or intuition. We believe the new generation of digital healthcare tools are required to integrate emerging technologies: artificial intelligence into portable platforms (mobile, wearables, etc.). One approach towards this goal could be the development of narrow use, but targeted and capable instrumentation. Therefore, in this thesis, we demonstrated the utilization of engineering techniques to build integrated, localized solutions. We initially applied deep learning technique to accelerate tissue imaging with OCT. With the help of generative adversarial networks, we suggest inpainting missing volumes. This tool could have practical implications for in vivo applications, including skin studies in the cosmetics industry. Later, we focused on the development of customized imaging setup with automated segmentation and quantitative analysis functionality targeting drug screening purposes. We believe the high-throughput element of the system brings unique value compared to previous methods. Finally, we suggest mobile, AI-powered otoscope device for non-specialist ear examination. This tool represents an engineering approach to cultivate smart and accessible aspects of digital healthcare devices, which could be vital to address the situation in low-resource communities and empower point-of-care diagnosis.clos

Evaluation of Skin Texture and Wrinkle Using Optical Coherence Tomography (Pilot Study)

Optical Coherence Tomography (OCT)??? ??? ????????? ?????? ????????? ???????????? ?????????????????? ????????????????????? ??? ?????? ????????? ???????????? ??? ??? ?????? ??????????????????. OCT ????????? ????????? ??????????????? ?????? ?????? ????????????????????? ???????????? ????????? ????????? ?????? ????????????????????? ????????? ?????? ??????????????? ??? ??? ??????. ??? ??????????????? OCT ??? ????????????????????? ???????????? ????????? ??? ?????? ????????? ?????? ???????????? ???????????????. ?????????(replica)??? ???????????? ??????????????? PRIMOS??? ????????? ????????? ?????? ????????? ????????? ????????? 3?????? ????????? ????????? ??????????????? PRIMOS????????? ???????????? ?????? ????????? ????????? ????????? ??? ?????????. ????????? ????????? ???????????? ????????? ?????? ????????? ????????? ???????????? ?????? ????????? ????????? ???????????? ?????????. ????????? ????????? ?????? OCT??? ??????????????????????????? ???????????? ??? ??? ????????? ?????? ??? ???????????? ????????? ?????? ??? ?????????????????? ?????? ???????????? ?????? ????????????

Fast and Quantitative Analysis of Xenopus Anatomy using Customized Imaging Scanner and PDMS Microwell

Xenopus is as an important and efficient animal model to study various diseases, because it readily manipulates the large numbers of embryos as well protein expression. Morphological evaluation of massing Xenopus at different stages is an essential procedure, but it currently requires labor-intensive and manual inspection under optical microscope. Here, we propose the high-throughput monitoring method via customized imaging tool and customized PDMS microwell plate. We also developed the home???built analysis software to accelerate the value of our device

Deep Learning-Based Glaucoma Screening Using Regional RNFL Thickness in Fundus Photography

Since glaucoma is a progressive and irreversible optic neuropathy, accurate screening and/or early diagnosis is critical in preventing permanent vision loss. Recently, optical coherence tomography (OCT) has become an accurate diagnostic tool to observe and extract the thickness of the retinal nerve fiber layer (RNFL), which closely reflects the nerve damage caused by glaucoma. However, OCT is less accessible than fundus photography due to higher cost and expertise required for operation. Though widely used, fundus photography is effective for early glaucoma detection only when used by experts with extensive training. Here, we introduce a deep learning-based approach to predict the RNFL thickness around optic disc regions in fundus photography for glaucoma screening. The proposed deep learning model is based on a convolutional neural network (CNN) and utilizes images taken with fundus photography and with RNFL thickness measured with OCT for model training and validation. Using a dataset acquired from normal tension glaucoma (NTG) patients, the trained model can estimate RNFL thicknesses in 12 optic disc regions from fundus photos. Using intuitive thickness labels to identify localized damage of the optic nerve head and then estimating regional RNFL thicknesses from fundus images, we determine that screening for glaucoma could achieve 92% sensitivity and 86.9% specificity. Receiver operating characteristic (ROC) analysis results for specificity of 80% demonstrate that use of the localized mean over superior and inferior regions reaches 90.7% sensitivity, whereas 71.2% sensitivity is reached using the global RNFL thicknesses for specificity at 80%. This demonstrates that the new approach of using regional RNFL thicknesses in fundus images holds good promise as a potential screening technique for early stage of glaucoma