藤田 大輔

BACKGROUND: Ultrasonography is used to diagnose osteochondritis dissecans (OCD) of the humerus; however, its reliability depends on the technical proficiency of the examiner. Recently, computer-aided diagnosis (CAD) using deep learning has been applied in the field of medical science, and high diagnostic accuracy has been reported. We aimed to develop a deep learning-based CAD system for OCD detection on ultrasound images and to evaluate the accuracy of OCD detection using the CAD system. METHODS: The CAD process comprises 2 steps: humeral capitellum detection using an object-detection algorithm and OCD classification using an image classification network. Four-directional ultrasound images of the elbow of the throwing arm of 196 baseball players (mean age, 11.2 years), including 104 players with normal findings and 92 with OCD, were used for training and validation. An external dataset of 20 baseball players (10 with normal findings and 10 with OCD) was used to evaluate the accuracy of the CAD system. A confusion matrix and the area under the receiver operating characteristic curve (AUC) were used to evaluate the system. RESULTS: Clinical evaluation using the external dataset resulted in high AUCs in all 4 directions: 0.969 for the anterior long axis, 0.966 for the anterior short axis, 0.996 for the posterior long axis, and 0.993 for the posterior short axis. The accuracy of OCD detection thus exceeded 0.9 in all 4 directions. CONCLUSIONS: We propose a deep learning-based CAD system to detect OCD lesions on ultrasound images. The CAD system achieved high accuracy in all 4 directions of the elbow. This CAD system with a deep learning model may be useful for OCD screening during medical checkups to reduce the probability of missing an OCD lesion. LEVEL OF EVIDENCE: Diagnostic Level II. See Instructions for Authors for a complete description of levels of evidence.

PURPOSE: Osteochondritis dissecans (OCD) of the humeral capitellum is a common cause of elbow disorders, particularly among young throwing athletes. Conservative treatment is the preferred treatment for managing OCD, and early intervention significantly influences the possibility of complete disease resolution. The purpose of this study is to develop a deep learning-based classification model in ultrasound images for computer-aided diagnosis. METHODS: This paper proposes a deep learning-based OCD classification method in ultrasound images. The proposed method first detects the humeral capitellum detection using YOLO and then estimates the OCD probability of the detected region probability using VGG16. We hypothesis that the performance will be improved by eliminating unnecessary regions. To validate the performance of the proposed method, it was applied to 158 subjects (OCD: 67, Normal: 91) using five-fold-cross-validation. RESULTS: The study demonstrated that the humeral capitellum detection achieved a mean average precision (mAP) of over 0.95, while OCD probability estimation achieved an average accuracy of 0.890, precision of 0.888, recall of 0.927, F1 score of 0.894, and an area under the curve (AUC) of 0.962. On the other hand, when the classification model was constructed for the entire image, accuracy, precision, recall, F1 score, and AUC were 0.806, 0.806, 0.932, 0.843, and 0.928, respectively. The findings suggest the high-performance potential of the proposed model for OCD classification in ultrasonic images. CONCLUSION: This paper introduces a deep learning-based OCD classification method. The experimental results emphasize the effectiveness of focusing on the humeral capitellum for OCD classification in ultrasound images. Future work should involve evaluating the effectiveness of employing the proposed method by physicians during medical check-ups for OCD.

Abstract Deep learning techniques for automatically detecting teeth in dental X-rays have gained popularity, providing valuable assistance to healthcare professionals. However, teeth detection in X-ray images is often hindered by alterations in tooth appearance caused by dental prostheses. To address this challenge, our paper proposes a novel method for teeth detection and numbering in dental panoramic X-rays, leveraging two separate CNN-based object detectors, namely YOLOv7, for detecting teeth and prostheses, alongside an optimization algorithm to refine the outcomes. The study utilizes a dataset of 3138 radiographs, of which 2553 images contain prostheses, to build a robust model. The tooth and prosthesis detection algorithms perform excellently, achieving mean average precisions of 0.982 and 0.983, respectively. Additionally, the trained tooth detection model is verified using an external dataset, and six-fold cross-validation is conducted to demonstrate the proposed method’s feasibility and robustness. Moreover, the investigation of performance improvement resulting from the inclusion of prosthesis information in the teeth detection process reveals a marginal increase in the average F1-score, rising from 0.985 to 0.987 compared to the sole teeth detection method. The proposed method is unique in its approach to numbering teeth as it incorporates prosthesis information and considers complete restorations such as dental implants and dentures of fixed bridges during the teeth enumeration process, which follows the universal tooth numbering system. These advancements hold promise for automating dental charting processes.

2019年現在便秘の有訴者率は34.8%と高く,加齢に伴って増加している.腹部X線画像から便秘の重症度やタイプを読影するためには習熟を要し,また主観的である.そのため,便秘の重症度や分類の手掛かりとすることを目的とし,ガスと便の量および所在を特定する技術が望まれている.腸管内ガスを定量的に評価する指標としてGVS(gas volume score)があるが,医師が手動で算出する必要があり,実臨床への導入は難しい.そこで本研究では便秘診断支援法として,U-Netを用いた腹部単純X線画像からの便とガス領域の自動抽出法を提案する.加えて,便における同様の指標SVS(stool volume score)と,ガス・便量を総合的に評価する指標JVS(joint volume score)を提案する.本自動化手法による抽出領域と熟練消化器内科医によるマスク領域を比較したところGVS,SVS,JVSがそれぞれ相関係数で0.895,0.879,0.586であった.また,同様に比較したところガス領域,便領域,ガスと便の結合領域においてDICE係数はそれぞれ最大0.650,0.474,0.640であった.(著者抄録)

脳血管疾患のうち代表的な脳内血種(ICH:intracerebral hematoma)は,発症から診断まで迅速な対応が求められる.診断には頭部CT画像からのICH領域の体積,形状,位置などが有効である.従来のICH領域抽出法はセマンティックセグメンテーションに基づき,頭部CT画像における脳内の高吸収領域を抽出するため,ICHと脳室内出血(IVH:intraventricular hemorrhage)の双方を有する症例において精度が低下する問題点がある.本研究では,イメージパッチ識別に基づく新しいICH領域抽出法を提案する.同手法は,ボクセルごとのconvolutional neural network(CNN)によるクラス分類により,高吸収領域におけるICHとIVHを識別する.提案法を評価するため,9例に適用した.交差検証はleave-one-out法を用いた.CNNは10層のOriginal CNN,VGG16,Resnet50を比較した.実験結果より,VGG16が最良で,テストデータに対して,Precision 83.0%,Accuracy 72.6%,F値81.6%であった.(著者抄録)

The incidence of falls in hospital facilities is high and can lead to a decrease in patients' quality of life and an increase in medical expenses. Therefore, the development of a system that can predict getting-up from a bed is necessary. This study proposes a get-up detecting sensor using 6-axis inertial sensor. This system can detect getting-up from a bed in real-time using machine learning with Edge AI. To evaluate the basic performance of the proposed system, a protocol was applied for four subjects, and data were collected. Alert accuracy rate and false alert rate were used as evaluation metrics, and a model was built using data from three of the subjects and evaluated with the remaining subject, which was repeated for all four subjects. For high-risk bed-leaving behavior, medium-risk pre-bed-leaving behavior, and low-risk get-up behavior, the alert accuracy rate (i.e., Recall) was 89.4%, 97.5%, and 86.3%, respectively, and the false alert rate (1-Precision) was 6.3%, 10.2%, and 0.0%, respectively. This confirmed the possibility of predicting rising behavior with high accuracy. Furthermore, as a proof of concept, a real-time get-up detection system was developed, and its practicality was demonstrated. Future challenges include reevaluating feature extraction and evaluating the performance of the proposed system with a diverse range of subjects of different ages, genders, and health statuses.

Intracerebral hematoma (ICH) is a blood clot that forms when a blood vessel in the brain ruptures for some reason and the spilled blood coagulates. ICH has a high morbidity and mortality rate, accounting for approximately 10% of all strokes. Manual segmentation of ICH in head CT images is very complicated, time consuming, and troublesome. When ICH perforates the ventricular wall and blood flows into the ventricle, there is little difference in CT value between ICH and intraventricular hemorrhage (IVH), and the boundary between them is unclear. Convolutional neural network (CNN) has proven to be a reliable method in the field of image recognition. In addition, quantification of ICH may aid in decision making in ICH treatment. In this study, we introduce CNN in a stepwise manner to differentiate ICH and IVH in the process and extract ICH regions. The results in 18 stroke patients show that our method is promising in the extraction of ICH regions with an accuracy of 75.2%.

Approximately 600,000 to 1,000,000 patients are diagnosed with rheumatoid arthritis (RA) in Japan. To provide appropriate treatment, it is necessary to accurately measure the progression of RA by diagnosing the disease several times a year. The modified total sharp score (mTSS) calculated from hand X-ray images is a standard diagnostic method for RA progression. However, this diagnostic method is time-consuming as the scores are rated at as many as 16 points per hand. Accordingly, in order to shorten the diagnosis time of RA patients and improve the quality of diagnosis, the development of computer-aided diagnosis (CAD) systems is expected. We have previously proposed a CAD system that can detect finger joint positions using a support vector machine and can estimate the mTSS using ridge regression. In this study, we propose a fully automatic detection method of RA score evaluation points in the carpal site from simple hand X-ray images using deep learning. The proposed method first segments the carpal site using deep learning. Next, the RA evaluation points are automatically determined from each segment based on prior knowledge. Experimental results on X-ray images of the hands of 140 patients with RA showed that the mTSS evaluation point at the carpal site could be detected with an average error of 25 pixels. This study enables the automatic detection of RA score evaluation points in the carpal site. In the diagnosis of RA, the time required for diagnosis can be reduced by automating the determination of diagnostic points by physician.

脳画像を用いて小児の脳疾患を診断する指標の一つとして,未発達や未熟児などの脳の正常な発達の進行が評価される。しかし,脳の発達度合いを定量的に推定する方法はなく,現状では医師の経験に基づいて診断が行われている.そのため,読影可能な医師の不足,定量性の欠如が問題である.本研究では,小児の脳CT画像から脳の発達年齢を予測する手法を提案する.この手法では,CT画像から頭蓋領域を抽出し,姿勢と位置の補正を行う.本研究では,3次元畳み込みニューラルネットワーク(3D CNN)を用いてCT画像から特徴を抽出し,全結合層で脳の発達年齢を予測する新しいネットワークモデルを提案した.このモデルの性能を,0歳から3歳までの脳神経医学的に異常のない小児60人を用いて評価した.予測年齢と患者の実年齢の間の平均平方根誤差は7.80(月)で、相関係数は0.801であった.(著者抄録)

現在,関節リウマチの国内患者数は60万人から100万人であると推定され,毎年数万人ずつ増加している.関節リウマチの診断には,手足のX線画像から算出するmTSS(modified Total Sharp Score)が標準的な診断基準として用いられているが,診断の問題点として,医師の目視による主観的診断であること,スコアの判断箇所が多いことが挙げられている.そのため,医師の負担軽減や診断の迅速性・正確性向上のためにmTSS自動で評価するコンピュータ支援診断(CAD)システムの開発が期待されている.本研究では,手関節リウマチのmTSS推定法に関して,リッジ回帰(RR)を用いた手法と,畳み込みニューラルネットワーク(CNN)の3つのモデル(VGG16,DenseNet201,Xception)を用いた手法の比較を行う.4手法の比較のために,90名のRA患者を対象に,手のX線画像を用いた実験を行った.実験結果は,erosionのmTSS予測はRR,JSNのmTSS予測はVGG16で最も良い結果が得られた.(著者抄録)