Journal of Medical and Biological Engineering

Zhang, Jianguo; Hou, Hu; Chen, Peng; Song, Liang; Hu, Fengling; Yu, Youcheng

2024 Journal of Medical and Biological Engineering

doi: 10.1007/s40846-024-00896-2

PurposeThe purpose of this study was to investigate the effects of implant spacing and placement angle on peri-implant bone stress using the finite element method.MethodsA model of the maxilla of an edentulous patient was obtained by computed tomography, and a splint prosthesis consisting of short or tilted implants was applied to the maxillary posterior region. Three spacings (15, 17, and 19 mm) were used for each restoration, and three placement angles (− 5, 0, and 5 degrees) were available at each spacing. Finite element analysis was used to evaluate the stress distributions and resonant frequencies of all the models by applying both vertical and oblique forces to the splint prostheses simultaneously.ResultsThe patters of stress distribution were better in the short implant group (19 mm with a − 5 degree placement angle) and the tilted implant group (19 mm with a 5 degree placement angle). In the short implant group, the difference in cortical bone stress (i.e., the difference between the maximum and minimum principal stresses) decreased from 248 MPa (15 mm, 5 degrees) to 116 MPa (19 mm, − 5 degrees), and the corresponding maximum von Mises stress in the implants was reduced by 35%. The resonance frequency of the short implant group (5300–5500 Hz) was slightly lower than that of the tilted implant group (5400–5700 Hz).ConclusionImplant spacing and the placement angle significantly affect the peri-implant bone stress distribution, proper implant placement is essential to minimize this risk.

Soe, Hsu Thiri; Iwata, Hiroyasu

2024 Journal of Medical and Biological Engineering

doi: 10.1007/s40846-024-00897-1

PurposeOwing to the increased mortality of heart diseases worldwide, especially myocardial infarction (MI), early detection is essential for improved diagnosis and treatment. The main purpose of this study is to develop a myocardial infarction detection method that combines deep learning and image processing, focusing on abnormalities in left ventricular (LV) wall motion.MethodsThe proposed method primarily uses the LV wall motion movement as a feature to train an LSTM network for MI detection. LV wall motion annotated by expert cardiologists was used as the ground truth. Accuracy, sensitivity, specificity, and area under the curve (AUC) were used to evaluate model performance. The proposed method primarily uses LV wall motion as a feature, combined with LV size and image pixels, to improve diagnostic accuracy over existing computer-aided design (CAD) systems.ResultsThe LSTM model achieved the highest diagnostic performance when trained on a combination of LV wall motion, LV size, and image pixel features with an accuracy of 95%, sensitivity of 96%, specificity of 94%, and an AUC value of 0.98. The LSTM model significantly outperformed models trained on individual feature sets or conventional machine learning algorithms. The inclusion of LV wall motion analysis improved accuracy by 10% compared to using only LV size and pixel data.ConclusionOur MI diagnosis system uses echocardiographic image analysis and LSTM-based deep learning to accurately detect LV wall motion issues related to MI. Compared with current CAD systems, the inclusion of LV wall motion analysis significantly improves diagnosis accuracy. The proposed system can help physicians detect MI early, thereby accelerating treatment and improving patient outcomes.

Liu, Hui; She, Qingshan; Lin, Jingchao; Chen, Qiang; Fang, Feng; Zhang, Yingchun

2024 Journal of Medical and Biological Engineering

doi: 10.1007/s40846-024-00895-3

PurposeLung cancer is the leading cause of cancer-related death. Early detection and treatment are crucial to improve survival rates. Radiologists determine whether the nodules are benign or malignant by observing their morphological attributes. However, this can be a challenging task for well-trained doctors.MethodsWe propose a more efficient automatic lung nodule analysis method, which establishes a clear cause-and-effect logic relationship between attribute features and malignancy features by incorporating multiple instance learning (MIL). The designed MIL classifier aggregates the learned instance weights and corresponding attribute features to form malignancy features. Compared to existing methods, it starts by mirroring the way radiologists observe nodules, then proceeds to extract the multi-scale morphological attribute characteristics of the nodules. The instance weight also serves as the attribute score of the attribute, providing a reference for consultation.ResultsOur method was validated using the LIDC-IDRI dataset and achieved an accuracy of 93.05% on benign-malignant classification task with the added capability of accurately scoring the attributes.ConclusionThe proposed method based on attribute score regression and multi-instance learning establishes the causal relationship between attribute scores and malignancy. This method improves accuracy in nodule classification and addresses the issue of poor model interpretability.