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Article ; Online: Minimizing the risk of injury to the popliteal artery during pullout repair of medial meniscus posterior root tears

Yuta Mori / Tomoaki Kamiya / Shinichiro Okimura / Kousuke Shiwaku / Yohei Okada / Atsushi Teramoto / Toshihiko Yamashita

Asia-Pacific Journal of Sports Medicine, Arthroscopy, Rehabilitation and Technology, Vol 35, Iss , Pp 81-

A cadaveric study

2024 Volume 84

Abstract: Background: The purpose of this study was to investigate the positional effect of guide pins used in the transtibial pullout repair of medial meniscus posterior root tears on the popliteal artery. Methods: We used eight cadaveric knees. Two 2.4-mm guide ... ...

Abstract	Background: The purpose of this study was to investigate the positional effect of guide pins used in the transtibial pullout repair of medial meniscus posterior root tears on the popliteal artery. Methods: We used eight cadaveric knees. Two 2.4-mm guide pins were inserted into the posterior root of the medial meniscus at 50° to the articular surface from the medial edge of the tibial tuberosity (anteromedial group) and the anterior edge of the medial collateral ligament (posteromedial group) using an aiming guide placed at the posterior root attachment of the medial meniscus from the anteromedial portal. The posterior capsule was dissected, and the popliteal artery was identified. The positional effect of the guide pins on the popliteal artery was photographed arthroscopically at 0°, 30°, 60°, and 90° knee flexion angles. The popliteal artery diameter and the minimum distance between the popliteal artery center and the guide pin tip were measured. Results: At 90° knee flexion, most of the guide pins in the anteromedial (6 knees; 75 %) and posteromedial groups (7 knees; 87.5 %) collided with the femoral intercondylar wall. The rate of collision was significantly higher at the 90° knee flexion position than that at other angles (p = 0.02). The average shortest distance between the popliteal artery center and the guide pin tip at 0° knee flexion in the posteromedial group (5.4 mm ± 3.4 mm) was significantly greater than that at other knee flexion angles, although the mean distance in the posteromedial group was so negligible that the guide pin could penetrate the popliteal artery. Conclusions: Knee flexion at 90° causes less damage to the popliteal artery during the transtibial pullout repair of medial meniscus posterior root tears.
Keywords	Medial meniscus posterior root tears ; Popliteal artery ; Transtibial pullout repair ; Sports medicine ; RC1200-1245
Subject code	796
Language	English
Publishing date	2024-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Prediction of Short-Term Mortality of Cardiac Care Unit Patients Using Image-Transformed ECG Waveforms

Terumasa Kondo / Atsushi Teramoto / Eiichi Watanabe / Yoshihiro Sobue / Hideo Izawa / Kuniaki Saito / Hiroshi Fujita

IEEE Journal of Translational Engineering in Health and Medicine, Vol 11, Pp 191-

2023 Volume 198

Abstract: Objective: The early detection of cardiac disease is important because the disease can lead to sudden death and poor prognosis. Electrocardiograms (ECG) are used to screen for cardiac diseases and are useful for the early detection and determination of ... ...

Abstract	Objective: The early detection of cardiac disease is important because the disease can lead to sudden death and poor prognosis. Electrocardiograms (ECG) are used to screen for cardiac diseases and are useful for the early detection and determination of treatment strategies. However, the ECG waveforms of cardiac care unit (CCU) patients with severe cardiac disease are often complicated by comorbidities and patient conditions, making it difficult to predict the severity of further cardiac disease. Therefore, this study predicts the short-term prognosis of CCU patients to detect further deterioration in CCU patients at an early stage. Methods: The ECG data (II, V3, V5, aVR induction) of CCU patients were converted to image data. The transformed ECG images were used to predict short-term prognosis with a two-dimensional convolutional neural network (CNN). Results: The prediction accuracy was 77.3%. Visualization by GradCAM showed that the CNN tended to focus on the shape and regularity of waveforms, such as heart failure and myocardial infarction. Conclusion: These results suggest that the proposed method may be useful for short-term prognosis prediction using the ECG waveforms of CCU patients. Clinical impact: The proposed method could be used to determine the treatment strategy and choose the intensity of treatment after admission to the CCU.
Keywords	Deep learning ; electrocardiogram ; GradCAM ; mortality prediction ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Medical technology ; R855-855.5
Subject code	610
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	IEEE
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Fully Automated Electronic Cleansing Using CycleGAN in Computed Tomography Colonography

Yoshitaka Isobe / Atsushi Teramoto / Fujio Morita / Kuniaki Saito / Hiroshi Fujita

Applied Sciences, Vol 12, Iss 10789, p

2022 Volume 10789

Abstract: In computed tomography colonography (CTC), an electric cleansing technique is used to mix barium with residual fluid, and colon residue is removed by image processing. However, a nonhomogenous mixture of barium and residue may not be properly removed. We ...

Abstract	In computed tomography colonography (CTC), an electric cleansing technique is used to mix barium with residual fluid, and colon residue is removed by image processing. However, a nonhomogenous mixture of barium and residue may not be properly removed. We developed an electronic cleansing method using CycleGAN, a deep learning technique, to assist diagnosis in CTC. In this method, an original computed tomography (CT) image taken during a CTC examination and a manually cleansed image in which the barium area was manually removed from the original CT image were prepared and converted to an image in which the barium was removed from the original CT image using CycleGAN. In the experiment, the electric cleansing images obtained using the conventional method were compared with those obtained using the proposed method. The average barium cleansing rates obtained by the conventional and proposed methods were 72.3% and 96.3%, respectively. A visual evaluation of the images showed that it was possible to remove only barium without removing the intestinal tract. Furthermore, the extraction of colorectal polyps and early stage cancerous lesions in the colon was performed as in the conventional method. These results indicate that the proposed method using CycleGAN may be useful for accurately visualizing the colon without barium.
Keywords	CT colonography ; CycleGAN ; deep learning ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
Subject code	006
Language	English
Publishing date	2022-10-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Development of a Fully Automated Glioma-Grading Pipeline Using Post-Contrast T1-Weighted Images Combined with Cloud-Based 3D Convolutional Neural Network

Hiroto Yamashiro / Atsushi Teramoto / Kuniaki Saito / Hiroshi Fujita

Applied Sciences, Vol 11, Iss 5118, p

2021 Volume 5118

Abstract: Glioma is the most common type of brain tumor, and its grade influences its treatment policy and prognosis. Therefore, artificial-intelligence-based tumor grading methods have been studied. However, in most studies, two-dimensional (2D) analysis and ... ...

Abstract	Glioma is the most common type of brain tumor, and its grade influences its treatment policy and prognosis. Therefore, artificial-intelligence-based tumor grading methods have been studied. However, in most studies, two-dimensional (2D) analysis and manual tumor-region extraction were performed. Additionally, deep learning research that uses medical images experiences difficulties in collecting image data and preparing hardware, thus hindering its widespread use. Therefore, we developed a 3D convolutional neural network (3D CNN) pipeline for realizing a fully automated glioma-grading system by using the pretrained Clara segmentation model provided by NVIDIA and our original classification model. In this method, the brain tumor region was extracted using the Clara segmentation model, and the volume of interest (VOI) created using this extracted region was assigned to a grading 3D CNN and classified as either grade II, III, or IV. Through evaluation using 46 regions, the grading accuracy of all tumors was 91.3%, which was comparable to that of the method using multi-sequence. The proposed pipeline scheme may enable the creation of a fully automated glioma-grading pipeline in a single sequence by combining the pretrained 3D CNN and our original 3D CNN.
Keywords	brain tumor ; magnetic resonance imaging ; grading ; convolutional neural network ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
Subject code	006
Language	English
Publishing date	2021-05-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Risk factors of lower extremity injuries in youth athletes

Takashi Matsumura / Hidenori Otsubo / Toshihiko Yamashita / Kota Watanabe / Tomoaki Kamiya / Atsushi Teramoto / Yasutoshi Ikeda

BMJ Open Sport & Exercise Medicine, Vol 9, Iss

2023 Volume 1

Abstract: Objective Lower extremity sports injuries frequently occur during an individual’s growth period. The object of the current study was to analyse the risk factors for lower extremity sports injuries for youth athletes. The secondary objective was to ... ...

Abstract	Objective Lower extremity sports injuries frequently occur during an individual’s growth period. The object of the current study was to analyse the risk factors for lower extremity sports injuries for youth athletes. The secondary objective was to clarify the factors related to new injuries after a lower extremity injury.Methods We extracted information on youth athletes (aged 10–15 years) with sports-related disorders. Background data and injury situations were collected via a specific application. During the follow-up period, new injuries were also recorded. The athletes were divided into two groups according to injury location (lower extremity or other). We performed a multiple logistic regression analysis to clarify the association between injury location and background data.Results 1575 complaints of lower extremity disorders and 328 complaints in other body parts were registered. According to the multiple regression analysis, practice time per week was significantly shorter for the lower extremity group than the other locations group (OR 0.98; 95% CI 0.963 to 0.999). Athletes whose future goal was at the recreational level had a significantly low incidence of new injuries after experiencing lower extremity disorders.Conclusion The practice environments and psychological factors should receive more attention to prevent lower extremity injuries.
Keywords	Medicine (General) ; R5-920
Subject code	796
Language	English
Publishing date	2023-02-01T00:00:00Z
Publisher	BMJ Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Automated Classification of Urinary Cells

Atsushi Teramoto / Ayano Michiba / Yuka Kiriyama / Eiko Sakurai / Ryoichi Shiroki / Tetsuya Tsukamoto

Applied Sciences, Vol 13, Iss 1763, p

Using Convolutional Neural Network Pre-trained on Lung Cells

2023 Volume 1763

Abstract: Urine cytology, which is based on the examination of cellular images obtained from urine, is widely used for the diagnosis of bladder cancer. However, the diagnosis is sometimes difficult in highly heterogeneous carcinomas exhibiting weak cellular atypia. ...

Abstract	Urine cytology, which is based on the examination of cellular images obtained from urine, is widely used for the diagnosis of bladder cancer. However, the diagnosis is sometimes difficult in highly heterogeneous carcinomas exhibiting weak cellular atypia. In this study, we propose a new deep learning method that utilizes image information from another organ for the automated classification of urinary cells. We first extracted 3137 images from 291 lung cytology specimens obtained from lung biopsies and trained a classification process for benign and malignant cells using VGG-16, a convolutional neural network (CNN). Subsequently, 1380 images were extracted from 123 urine cytology specimens and used to fine-tune the CNN that was pre-trained with lung cells. To confirm the effectiveness of the proposed method, we introduced three different CNN training methods and compared their classification performances. The evaluation results showed that the classification accuracy of the fine-tuned CNN based on the proposed method was 98.8% regarding sensitivity and 98.2% for specificity of malignant cells, which were higher than those of the CNN trained with only lung cells or only urinary cells. The evaluation results showed that urinary cells could be automatically classified with a high accuracy rate. These results suggest the possibility of building a versatile deep-learning model using cells from different organs.
Keywords	urinary cell ; classification ; deep learning ; convolutional neural network ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
Subject code	006
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Automated detection scheme for acute myocardial infarction using convolutional neural network and long short-term memory.

Ryosuke Muraki / Atsushi Teramoto / Keiko Sugimoto / Kunihiko Sugimoto / Akira Yamada / Eiichi Watanabe

PLoS ONE, Vol 17, Iss 2, p e

2022 Volume 0264002

Abstract: The early detection of acute myocardial infarction, which is caused by lifestyle-related risk factors, is essential because it can lead to chronic heart failure or sudden death. Echocardiography, among the most common methods used to detect acute ... ...

Abstract	The early detection of acute myocardial infarction, which is caused by lifestyle-related risk factors, is essential because it can lead to chronic heart failure or sudden death. Echocardiography, among the most common methods used to detect acute myocardial infarction, is a noninvasive modality for the early diagnosis and assessment of abnormal wall motion. However, depending on disease range and severity, abnormal wall motion may be difficult to distinguish from normal myocardium. As abnormal wall motion can lead to fatal complications, high accuracy is required in its detection over time on echocardiography. This study aimed to develop an automatic detection method for acute myocardial infarction using convolutional neural networks (CNNs) and long short-term memory (LSTM) in echocardiography. The short-axis view (papillary muscle level) of one cardiac cycle and left ventricular long-axis view were input into VGG16, a CNN model, for feature extraction. Thereafter, LSTM was used to classify the cases as normal myocardium or acute myocardial infarction. The overall classification accuracy reached 85.1% for the left ventricular long-axis view and 83.2% for the short-axis view (papillary muscle level). These results suggest the usefulness of the proposed method for the detection of myocardial infarction using echocardiography.
Keywords	Medicine ; R ; Science ; Q
Subject code	610
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Automated Extraction of Cerebral Infarction Region in Head MR Image Using Pseudo Cerebral Infarction Image by CycleGAN

Mizuki Yoshida / Atsushi Teramoto / Kohei Kudo / Shoji Matsumoto / Kuniaki Saito / Hiroshi Fujita

Applied Sciences, Vol 12, Iss 489, p

2022 Volume 489

Abstract: Since recognizing the location and extent of infarction is essential for diagnosis and treatment, many methods using deep learning have been reported. Generally, deep learning requires a large amount of training data. To overcome this problem, we ... ...

Abstract	Since recognizing the location and extent of infarction is essential for diagnosis and treatment, many methods using deep learning have been reported. Generally, deep learning requires a large amount of training data. To overcome this problem, we generated pseudo patient images using CycleGAN, which performed image transformation without paired images. Then, we aimed to improve the extraction accuracy by using the generated images for the extraction of cerebral infarction regions. First, we used CycleGAN for data augmentation. Pseudo-cerebral infarction images were generated from healthy images using CycleGAN. Finally, U-Net was used to segment the cerebral infarction region using CycleGAN-generated images. Regarding the extraction accuracy, the Dice index was 0.553 for U-Net with CycleGAN, which was an improvement over U-Net without CycleGAN. Furthermore, the number of false positives per case was 3.75 for U-Net without CycleGAN and 1.23 for U-Net with CycleGAN, respectively. The number of false positives was reduced by approximately 67% by introducing the CycleGAN-generated images to training cases. These results indicate that utilizing CycleGAN-generated images was effective and facilitated the accurate extraction of the infarcted regions while maintaining the detection rate.
Keywords	cerebral infarction ; CycleGAN ; deep learning ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
Subject code	006
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Lung cancer CT image generation from a free-form sketch using style-based pix2pix for data augmentation

Ryo Toda / Atsushi Teramoto / Masashi Kondo / Kazuyoshi Imaizumi / Kuniaki Saito / Hiroshi Fujita

Scientific Reports, Vol 12, Iss 1, Pp 1-

2022 Volume 10

Abstract: Abstract Artificial intelligence (AI) applications in medical imaging continue facing the difficulty in collecting and using large datasets. One method proposed for solving this problem is data augmentation using fictitious images generated by generative ...

Abstract	Abstract Artificial intelligence (AI) applications in medical imaging continue facing the difficulty in collecting and using large datasets. One method proposed for solving this problem is data augmentation using fictitious images generated by generative adversarial networks (GANs). However, applying a GAN as a data augmentation technique has not been explored, owing to the quality and diversity of the generated images. To promote such applications by generating diverse images, this study aims to generate free-form lesion images from tumor sketches using a pix2pix-based model, which is an image-to-image translation model derived from GAN. As pix2pix, which assumes one-to-one image generation, is unsuitable for data augmentation, we propose StylePix2pix, which is independently improved to allow one-to-many image generation. The proposed model introduces a mapping network and style blocks from StyleGAN. Image generation results based on 20 tumor sketches created by a physician demonstrated that the proposed method can reproduce tumors with complex shapes. Additionally, the one-to-many image generation of StylePix2pix suggests effectiveness in data-augmentation applications.
Keywords	Medicine ; R ; Science ; Q
Subject code	006 ; 004
Language	English
Publishing date	2022-07-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Reliability and validity of the Forgotten Joint Score-12 for total ankle replacement and ankle arthrodesis.

Koji Noguchi / Satoshi Yamaguchi / Atsushi Teramoto / Kentaro Amaha / Noriyuki Kanzaki / Hirofumi Tanaka / Tetsuro Yasui / Yosuke Inaba

PLoS ONE, Vol 18, Iss 6, p e

2023 Volume 0286762

Abstract: Objectives This study evaluated the reliability and validity of the Forgotten Joint Score-12 (FJS-12)-a measure of patients' ability to forget their joints in daily life-in patients who underwent total ankle replacement (TAR) or ankle arthrodesis (AA). ... ...

Abstract	Objectives This study evaluated the reliability and validity of the Forgotten Joint Score-12 (FJS-12)-a measure of patients' ability to forget their joints in daily life-in patients who underwent total ankle replacement (TAR) or ankle arthrodesis (AA). Methods Patients who underwent TAR or AA were recruited from seven hospitals. The patients completed the Japanese version of FJS-12 twice, at an interval of two weeks, at a minimum of one year postoperatively. Additionally, they answered the Self-Administered Foot Evaluation Questionnaire and EuroQoL 5-Dimension 5-Level as comparators. The construct validity, internal consistency, test-retest reliability, measurement error, and floor and ceiling effects were evaluated. Results A total of 115 patients (median age, 72 years), comprising 50 and 65 patients in the TAR and AA groups respectively, were evaluated. The mean FJS-12 scores were 65 and 58 for the TAR and AA groups, respectively, with no significant difference between groups (P = 0.20). Correlations between the FJS-12 and Self-Administered Foot Evaluation Questionnaire subscale scores were good to moderate. The correlation coefficient ranged from 0.39 to 0.71 and 0.55 to 0.79 in the TAR and AA groups, respectively. The correlation between the FJS-12 and EuroQoL 5-Dimension 5-Level scores was poor in both groups. The internal consistency was adequate, with Cronbach's α greater than 0.9 in both groups. The intraclass correlation coefficients of test-retest reliability was 0.77 and 0.98 in the TAR and AA groups, respectively. The 95% minimal detectable change values were 18.0 and 7.2 points in the TAR and AA groups, respectively. No floor or ceiling effect was observed in either group. Conclusions The Japanese version of FJS-12 is a valid and reliable questionnaire for measuring joint awareness in patients with TAR or AA. The FJS-12 can be a useful tool for the postoperative assessment of patients with end-stage ankle arthritis.
Keywords	Medicine ; R ; Science ; Q
Subject code	796
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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