Background: Lumbar disc herniation (LDH) is among the most common causes of lower back pain and sciatica. The causes of LDH have not been fully elucidated but most likely involve a complex combination of mechanical and biological processes. Magnetic resonance imaging (MRI) is a tool most frequently used for LDH because it can show abnormal soft tissue areas around the spine. Deep learning models may be trained to recognize images with high speed and accuracy to diagnose LDH. Although the deep learning model requires huge numbers of image datasets to train and establish the best model, this study processed enhanced medical image features for training the small-scale deep learning dataset.Methods: We propose automatic detection to assist the initial LDH exam for lower back pain. The subjects were between 20 and 65 years old with at least 6 months of work experience. The deep learning method employed the YOLOv3 model to train and detect small object changes such as LDH on MRI. The dataset images were processed and combined with labeling and annotation from the radiologist’s diagnosis record.Results: Our method proves the possibility of using deep learning with a small-scale dataset with limited medical images. The highest mean average precision (mAP) was 92.4% at 550 images with data augmentation (550-aug), and the YOLOv3 LDH training was 100% with the best average precision at 550-aug among all datasets. This study used data augmentation to prevent under- or overfitting in an object detection model that was trained with the small-scale dataset.Conclusions: The data augmentation technique plays a crucial role in YOLOv3 training and detection results. This method displays a high possibility for rapid initial tests and auto-detection for a limited clinical dataset.

Diabetic foot ulcers are one of the most serious complications associated with diabetes mellitus. Current research studies have demonstrated that biomechanical alterations of the diabetic foot contribute to the development of foot ulcers. However, the changes of soft tissue biomechanical properties associated with diabetes and its influences on the development of diabetic foot ulcers have not been investigated. The purpose of this study was to investigate the effect of diabetes on the biomechanical properties of plantar soft tissues and the relationship between biomechanical properties and plantar pressure distributions. We used the ultrasound indentation tests to measure force-deformation relationships of plantar soft tissues and calculate the effective Young's modulus and quasi-linear viscoelastic parameters to quantify biomechanical properties of plantar soft tissues. We also measured plantar pressures to calculate peak plantar pressure and plantar pressure gradient. Our results showed that diabetics had a significantly greater effective Young's modulus and initial modulus of quasi-linear viscoelasticity compared to non-diabetics. The plantar pressure gradient and biomechanical properties were significantly correlated. Our findings indicate that diabetes is linked to an increase in viscoelasticity of plantar soft tissues that may contribute to a higher peak plantar pressure and plantar pressure gradient in the diabetic foot.

Among people with diabetes mellitus (DM), the two common strategies for decreasing peak plantar pressure (PPP) to reduce diabetic foot ulcers (DFUs) risks are to modify walking speeds and to change insole materials. This study reviewed the PPP reduction based on various walking speeds and insole materials. The articles were retrieved from four major scientific databases and manual search. We identified 1585 articles, of which 27 articles were selected for full-text analysis. We found that in faster walking speeds, the forefoot PPP was higher (308 kPa) than midfoot (150 kPa) and rearfoot (251 kPa) PPP. The appropriate walking speed for reducing the forefoot PPP was about 6 km/h for non-DM and 4 km/h for DM people. The forefoot PPP in DM people was 185% higher than that of non-DM people. Ethylene–vinyl acetate (EVA) insole material was the most popular material used by experts (26%) in the forefoot and reduced 37% of PPP. In conclusion, the suitable walking speed for DM was slower than for non-DM people, and EVA was the most common insole material used to decrease the PPP under the forefoot. The clinicians might recommend DM people to walk at 4 km/h and wear EVA insole material to minimize the DFUs.

Background:In magnetic resonance imaging (MRI), lumbar disc herniation (LDH) detection is challenging due to the various shapes, sizes, angles, and regions associated with bulges, protrusions, extrusions, and sequestrations. Lumbar abnormalities in MRI can be detected automatically by using deep learning methods. As deep learning models gain recognition, they may assist in diagnosing LDH with MRI images and provide initial interpretation in clinical settings. YOU ONLY LOOK ONCE (YOLO) model series are often used to train deep learning algorithms for real-time biomedical image detection and prediction. This study aims to confirm which YOLO models (YOLOv5, YOLOv6, and YOLOv7) perform well in detecting LDH in different regions of the lumbar intervertebral disc.Materials and methods:The methodology involves several steps, including converting DICOM images to JPEG, reviewing and selecting MRI slices for labeling and augmentation using ROBOFLOW, and constructing YOLOv5x, YOLOv6, and YOLOv7 models based on the dataset. The training dataset was combined with the radiologist’s labeling and annotation, and then the deep learning models were trained using the training/validation dataset.Results:Our result showed that the 550-dataset with augmentation (AUG) or without augmentation (non-AUG) in YOLOv5x generates satisfactory training performance in LDH detection. The AUG dataset overall performance provides slightly higher accuracy than the non-AUG. YOLOv5x showed the highest performance with 89.30% mAP compared to YOLOv6, and YOLOv7. Also, YOLOv5x in non-AUG dataset showed the balance LDH region detections in L2-L3, L3-L4, L4-L5, and L5-S1 with above 90%. And this illustrates the competitiveness of using non-AUG dataset to detect LDH.Conclusion:Using YOLOv5x and the 550 augmented dataset, LDH can be detected with promising both in non-AUG and AUG dataset. By utilizing the most appropriate YOLO model, clinicians have a greater chance of diagnosing LDH early and preventing adverse effects for their patients.

Alzheimer’s disease (AD) is a major public health priority. Hippocampus is one of the most affected areas of the brain and is easily accessible as a biomarker using MRI images in machine learning for diagnosing AD. In machine learning, using entire MRI image slices showed lower accuracy for AD classification. We present the select slices method by landmarks on the hippocampus region in MRI images. This study aims to see which views of MRI images have higher accuracy for AD classification. Then, to get the value of three views and categories, we used multiclass classification with the publicly available Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset using Resnet50 and LeNet. The models were used in a total dataset of 4,500 MRI slices in three views and categories. Our study demonstrated that the selecting slices performed better than using entire slices in MRI images for AD classification. Our method improves the accuracy of machine learning, and the coronal view showed higher accuracy. This method played a significant role in improving the accuracy of machine learning performance. The results for the coronal view were similar to the medical experts usually used to diagnose AD. We also found that LeNet models became the potential model for AD classification.

The most frequent clinical complication is diabetes. Diabetes is characterized by elevated blood glucose levels resulting in sensory nerve damage or lesions. Diabetic foot wounds are often slow to heal and require medical attention and monitoring. This study evaluates the effect of far-infrared radiation on the microcirculation and plantar pressure in the diabetic foot. Ten diabetics and 4 nondiabetics were recruited in this study. The diabetic group was examined before and after the intervention in each month for 3 consecutive months. Four nondiabetic groups were also measured before and after the intervention for 2 weeks in each month. The surface temperature and blood flow in the diabetic foot was significantly improved (temperature: 32.1 ± 2.3°C vs 33.5 ± 2.2°C, P < .05; blood flow image: 118.3 ± 58.1 PU [perfusion unit] vs 50.4 ± 4.3 PU, P < .05). The sympathetic nerve activity index LF also increased from 40.8 ± 18.6% to 61.8 ± 13.5% (P = .07) in the second month. Plantar pressure tended to increase in the third month. This might indicate that far-infrared radiation could affect the mechanical properties of the plantar foot soft tissue. These results indicated that the effects of far-infrared radiation would improve blood circulation and change the soft tissue properties in the diabetic foot.