(1) Background: The misuse of transformation technology using medical images is a critical problem that can endanger patients’ lives, and detecting manipulation via a deep learning model is essential to address issues of manipulated medical images that may arise in the healthcare field. (2) Methods: The dataset was divided into a real fundus dataset and a manipulated dataset. The fundus image manipulation detection model uses a deep learning model based on a Convolution Neural Network (CNN) structure that applies a concatenate operation for fast computation speed and reduced loss of input image weights. (3) Results: For real data, the model achieved an average sensitivity of 0.98, precision of 1.00, F1-score of 0.99, and AUC of 0.988. For manipulated data, the model recorded sensitivity of 1.00, precision of 0.84, F1-score of 0.92, and AUC of 0.988. Comparatively, five ophthalmologists achieved lower average scores on manipulated data: sensitivity of 0.71, precision of 0.61, F1-score of 0.65, and AUC of 0.822. (4) Conclusions: This study presents the possibility of addressing and preventing problems caused by manipulated medical images in the healthcare field. The proposed approach for detecting manipulated fundus images through a deep learning model demonstrates higher performance than that of ophthalmologists, making it an effective method.

This paper presents a classification model for eye diseases utilizing attention mechanisms to learn features from fundus images and structures. The study focuses on diagnosing glaucoma by extracting retinal vessels and the optic disc from fundus images using a ResU-Net-based segmentation model and Hough Circle Transform, respectively. The extracted structures and preprocessed images were inputted into a CNN-based multi-input model for training. Comparative evaluations demonstrated that our model outperformed other research models in classifying glaucoma, even with a smaller dataset. Ablation studies confirmed that using attention mechanisms to learn fundus structures significantly enhanced performance. The study also highlighted the challenges in normal case classification due to potential feature degradation during structure extraction. Future research will focus on incorporating additional fundus structures such as the macula, refining extraction algorithms, and expanding the types of classified eye diseases.

2015년부터 국토부와 국토지리정보원의 꾸준한 정책 덕분에 정밀도로지도를 신규로 구축하는 방법 및 기술은 발전하였으며 이제는 기존 지도를 어떻게 갱신할 것인지가 이슈로 집중되고 있다. 대부분 AI를 활용하여 영상 기준으로 도로 주변 시설물을 자동 인지하는 연구가 많이 진행하고 있으나 해당 시설물의 절대 위치를 추출하는 연구는 찾아보기 어렵다. 본 연구는 차량용 MMS (Mobile Mapping System) 데이터를 활용하여 도로 주변 시설물을 자동으로 인지하고 인지한 객체를 점군데이터와 중첩시켜 해당 시설물의 절대 위치를 추출하는 연구를 진행하였으며, 결과적으로 추출한 시설물의 위치가 기 구축되어 있는 정밀도로지도와 비교 분석하였을 때 1m 이내의 이격거리를 갖는지 확인하였다. 이는 정밀도로지도 정확도 오차 기준 0.25m 이상으로 정밀도로지도 갱신 부분에 적용하기에는 아직 부족한 부분이 있으나, 가능성은 확인하였으며 추가적인 연구로 기술 고도화를 이루어 추후 활용 가능할 것으로 판단된다.

(1) Background: The misuse of transformation technology using medical images is a critical problem that can endanger patients’ lives, and detecting manipulation via a deep learning model is essential to address issues of manipulated medical images that may arise in the healthcare field. (2) Methods: The dataset was divided into a real fundus dataset and a manipulated dataset. The fundus image manipulation detection model uses a deep learning model based on a Convolution Neural Network (CNN) structure that applies a concatenate operation for fast computation speed and reduced loss of input image weights. (3) Results: For real data, the model achieved an average sensitivity of 0.98, precision of 1.00, F1-score of 0.99, and AUC of 0.988. For manipulated data, the model recorded sensitivity of 1.00, precision of 0.84, F1-score of 0.92, and AUC of 0.988. Comparatively, five ophthalmologists achieved lower average scores on manipulated data: sensitivity of 0.71, precision of 0.61, F1-score of 0.65, and AUC of 0.822. (4) Conclusions: This study presents the possibility of addressing and preventing problems caused by manipulated medical images in the healthcare field. The proposed approach for detecting manipulated fundus images through a deep learning model demonstrates higher performance than that of ophthalmologists, making it an effective method.

This paper presents a classification model for eye diseases utilizing attention mechanisms to learn features from fundus images and structures. The study focuses on diagnosing glaucoma by extracting retinal vessels and the optic disc from fundus images using a ResU-Net-based segmentation model and Hough Circle Transform, respectively. The extracted structures and preprocessed images were inputted into a CNN-based multi-input model for training. Comparative evaluations demonstrated that our model outperformed other research models in classifying glaucoma, even with a smaller dataset. Ablation studies confirmed that using attention mechanisms to learn fundus structures significantly enhanced performance. The study also highlighted the challenges in normal case classification due to potential feature degradation during structure extraction. Future research will focus on incorporating additional fundus structures such as the macula, refining extraction algorithms, and expanding the types of classified eye diseases.

2015년부터 국토부와 국토지리정보원의 꾸준한 정책 덕분에 정밀도로지도를 신규로 구축하는 방법 및 기술은 발전하였으며 이제는 기존 지도를 어떻게 갱신할 것인지가 이슈로 집중되고 있다. 대부분 AI를 활용하여 영상 기준으로 도로 주변 시설물을 자동 인지하는 연구가 많이 진행하고 있으나 해당 시설물의 절대 위치를 추출하는 연구는 찾아보기 어렵다. 본 연구는 차량용 MMS (Mobile Mapping System) 데이터를 활용하여 도로 주변 시설물을 자동으로 인지하고 인지한 객체를 점군데이터와 중첩시켜 해당 시설물의 절대 위치를 추출하는 연구를 진행하였으며, 결과적으로 추출한 시설물의 위치가 기 구축되어 있는 정밀도로지도와 비교 분석하였을 때 1m 이내의 이격거리를 갖는지 확인하였다. 이는 정밀도로지도 정확도 오차 기준 0.25m 이상으로 정밀도로지도 갱신 부분에 적용하기에는 아직 부족한 부분이 있으나, 가능성은 확인하였으며 추가적인 연구로 기술 고도화를 이루어 추후 활용 가능할 것으로 판단된다.

In the medical field, the abuse of manipulation data through image processing technology of deep learning is fatal. Therefore, research on detection of modulation on medical images is essential. The data set for fundus data manipulation used 356 right fundus images of 4 lesions (normal, diabetic retinopathy, glaucoma, macular degeneration) out of about 6,000 data collected by Shangong Medical Technology Co., Ltd. The training and verification dataset of the manipulation detection model used original data and U-Net manipulation data. In addition, data manipulated in the Cycle General Adversarial Network (GAN) model were used for the diversity of verification. In this paper, three ophthalmologists and two general doctors were asked to verify the above modulation data. Verification was requested for each lesion, and the verification results were shown through the Receiver operating characteristic (ROC) curve and the Area Under the Curve (AUC). The verification of this study evaluated a total of 100 randomly extracted manipulation data and original data as Observer Performance Test (OPT) for each group. When the evaluation results were digitized as average scores, the scores of ophthalmologists group: 0.72 and general doctors group: 0.67 were recorded. The manipulated images were so similar that both ophthalmologists and general doctors could not find about 30%. However, the manipulation detection model studied in this paper was excellent in about 20% of the group OPT score with a lesion average of 0.913 in the same data group. Therefore, it can be seen that the manipulation detection model of this study finds the manipulated image and the original image well. The future plan is to expand the scope of manipulation detection data to conduct research on various medical data. After that, it will verify its availability at the actual site.

Motivation: Benign paroxysmal positional vertigo (BPPV) is characterized by torsional nystagmus induced by changes in head position, where accurate quantitative assessment of subtle torsional eye movements is essential for precise diagnosis. Conventional videonystagmography (VNG) techniques face challenges in accurately capturing the rotational components of pupil movements, and existing automated methods typically exhibit limited performance in identifying torsional nystagmus. Methodology: The objective of this study was to develop an automated system capable of accurately and quantitatively detecting torsional nystagmus. We introduce the Torsion Transformer model, designed to directly estimate torsion angles from iris images. This model employs a self-supervised learning framework comprising two main components: a Decoder module, which learns rotational transformations from image data, and a Finder module, which subsequently estimates the torsion angle. The resulting torsion angle data, represented as time-series, are then analyzed using a 1-dimensional convolutional neural network (1D-CNN) classifier to detect the presence of nystagmus. The performance of the proposed method was evaluated using video recordings from 127 patients diagnosed with BPPV. Findings: Our Torsion Transformer model demonstrated robust performance, achieving a sensitivity of 89.99%, specificity of 86.36%, an F1-score of 88.82%, and an area under the receiver operating characteristic curve (AUROC) of 87.93%. These results indicate that the proposed model effectively quantifies torsional nystagmus, with performance levels comparable to established methods for detecting horizontal and vertical nystagmus. Thus, the Torsion Transformer shows considerable promise as a clinical decision support tool in the diagnosis of BPPV. Key Findings: Technical performance improvement in torsional nystagmus detection; System to support clinical decision-making for healthcare professionals.

Background: In this study, we explored the potential of predicting dysglycemia in patients who need to continuously manage blood glucose levels using a non-invasive method via electrocardiography (ECG). Methods: The data were collected from patients with diabetes, and heart rate variability (HRV) features were extracted via ECG processing. A residual block-based one-dimensional convolution neural network model was used to predict dysglycemia. Results: The dysglycemia prediction results at each time point, including at the time of blood glucose measurement, 15 min prior to measurement, and 30 min prior to measurement, exhibited no significant differences compared with the blood glucose measurement values. This result confirmed that the proposed artificial intelligence model for dysglycemia prediction performed well at each time point. Additionally, to determine the optimal number of features required for predicting dysglycemia, 77 HRV features were individually eliminated in the order of decreasing importance with respect to the prediction accuracy; the optimal number of features for the model to predict dysglycemia was determined to be 12. The dysglycemia prediction results obtained 30 min prior to measurement, which exhibited the highest prediction range in this study, were as follows: accuracy = 90.5, sensitivity = 87.52, specificity = 92.74, and precision = 89.86. Conclusions: Furthermore, we determined that no significant differences exist in the blood glucose prediction results reported in previous studies, wherein various vital signs and blood glucose values were used as model inputs, and the results obtained in this study, wherein only ECG data were used to predict dysglycemia.

본 논문은 환자의 증상으로부터 의사의 최종치료를 위한 진단결정에 이르는 이유를 묻는 진단프로세스가 각 단계에서 내리는 질의와 대답 (Question and Answer)을 통하여 이루어진다는 점에 주목한다. 이에 따라 각 단계의 질의 (question)을 만드는 모델을 제시한다. 본 모델은 전문의에 의한 임상 진단의 흐름을 반영하여 각 단계별 질문으로 구성하였다. 진단 과정의 인과관계를 추론하기 위해 수행되었으며 다양한 Task를 구성하기 위한 전문의의 분석과정을 포함하였다. 이는 각 환자의 증상에 대하여 의사가 내린 진단결정으로 데이터의 설명가능성을 보장할 수 있으며, 추론적 접근이 가능하다. 최종적으로 의사의 질의와 대답에 대한 보고서와 전문가의 수동 주석을 통한 보고서를 비교 평가한다. 평가는 baseline-model로 Visual commonsense reasoning model, VI-bert, Biobert를 사용한다. 그 결과 BLEU-1 to 2의 평균 성능은 각각 3.5904, 2.8007, 3.4627를 보였으며, rogue-1, 2, l의 평균 성능은 각각 0.1606, 0.3428, 0.0797를 보였다. 이는 보고서 생성에서 데이터에 의료 지식이 필요하다는 것을 보여주며, 인과관계를 학습하기 위한 모델에 적용할 수 있는 설명가능한 데이터라고 볼 수 있다.

This paper presents the relayed distribution of ATSC 3.0 broadcast signals to mobile users in moving vehicles.The gateway relay featured in this work seamlessly converts received ATSC 3.0 signals into a WiFi interface.This proposal exhibits the use of an ATSC 3.0-to-WiFi gateway to amplify broadcasting media in automotive, allowing personalized experience on individual seat positions. Index Terms-ATSC 3.0, in