Diabetic foot ulcers (DFUs) are considered the most challenging forms of chronic ulcerations to handle their multifactorial nature. It is necessary to establish a comprehensive treatment plan, accurate, and systematic evaluation of a patient with a DFU. This paper proposed an image recognition of diabetic foot wounds to support the effective execution of the treatment plan. In the severity of a diabetic foot ulcer, we refer to the current qualitative evaluation method commonly used in clinical practice, developed by the International Working Group on the Diabetic Foot: PEDIS index, and the evaluation made by physicians. The deep neural network, convolutional neural network, object recognition, and other technologies are applied to analyze the classification, location, and size of wounds by image analysis technology. The image features are labeled with the help of the physician. The Object Detection Fast R-CNN method is applied to these wound images to build and train machine learning modules and evaluate their effectiveness. In the assessment accuracy, it can be indicated that the wound image detection data can be as high as 90%.

With conventional stethoscopes, the auscultation results may vary from one doctor to another due to a decline in his/her hearing ability with age or his/her different professional training, and the problematic cardiopulmonary sound cannot be recorded for analysis. In this paper, to resolve the above-mentioned issues, an electronic stethoscope was developed consisting of a traditional stethoscope with a condenser microphone embedded in the head to collect cardiopulmonary sounds and an AI-based classifier for cardiopulmonary sounds was proposed. Different deployments of the microphone in the stethoscope head with amplification and filter circuits were explored and analyzed using fast Fourier transform (FFT) to evaluate the effects of noise reduction. After testing, the microphone placed in the stethoscope head surrounded by cork is found to have better noise reduction. For classifying normal (healthy) and abnormal (pathological) cardiopulmonary sounds, each sample of cardiopulmonary sound is first segmented into several small frames and then a principal component analysis is performed on each small frame. The difference signal is obtained by subtracting PCA from the original signal. MFCC (Mel-frequency cepstral coefficients) and statistics are used for feature extraction based on the difference signal, and ensemble learning is used as the classifier. The final results are determined by voting based on the classification results of each small frame. After the testing, two distinct classifiers, one for heart sounds and one for lung sounds, are proposed. The best voting for heart sounds falls at 5–45% and the best voting for lung sounds falls at 5–65%. The best accuracy of 86.9%, sensitivity of 81.9%, specificity of 91.8%, and F1 score of 86.1% are obtained for heart sounds using 2 s frame segmentation with a 20% overlap, whereas the best accuracy of 73.3%, sensitivity of 66.7%, specificity of 80%, and F1 score of 71.5% are yielded for lung sounds using 5 s frame segmentation with a 50% overlap.

Purpose: To effectively perform quantification of brain normal tissues and pathologies simultaneously, independent component analysis (ICA) coupled with support vector machine (SVM) is investigated and evaluated for effective volumetric measurements of normal and lesion tissues using multispectral MR images. Materials and Methods: Synthetic and real MR data of normal brain and white matter lesion (WML) data were used to evaluate the accuracy and reproducibility of gray matter (GM), white matter (WM), and WML volume measurements by using the proposed ICA+SVM method to analyze three sets of MR images, T1‐weighted, T2‐weighted, and proton density/fluid‐attenuated inversion recovery images. Results: The Tanimoto indexes of GM/WM classification in the normal synthetic data calculated by the ICA+SVM method were 0.82/0.89 for data with 0% noise level. As for clinical MR data experiments, the ICA+SVM method clearly extracted the normal tissues and white matter hyperintensity lesions from the MR images, with low intra‐ and inter‐operator coefficient of variations. Conclusion: The experiments conducted provide evidence that the ICA+SVM method has shown promise and potential in applications to classification of normal and pathological tissues in brain MRI. J. Magn. Reson. Imaging 2010;32:24–34.

Objective: Hyperspectral imaging (HSI) is a novel technology for obtaining quantitative measurements from transcutaneous spatial and spectral information. In patients with SSc, the severity of skin tightness is associated with internal organ involvement. However, clinical assessment using the modified Rodnan skin score is highly variable and there are currently no universal standardized protocols. This study aimed to compare the ability to differentiate between SSc patients and healthy controls using skin scores, ultrasound and HSI. Methods: Short-wave infrared light was utilized to detect the spectral angle mapper (SAM) of HSI. In addition, skin severity was evaluated by skin scores, ultrasound to detect dermal thickness and strain elastography. Spearman’s correlation was used for assessing skin scores, strain ratio, thickness and SAM. Comparisons of various assessment tools were performed by receiver operating characteristic curves. Results: In total, 31 SSc patients were enrolled. SAM was positively correlated with skin scores and dermal thickness. In SSc patients with normal skin scores, SAM values were still significantly higher than in healthy controls. SAM exhibited the highest area under the curve (AUC: 0.812, P < 0.001) in detecting SSc compared with skin scores (AUC: 0.712, P < 0.001), thickness (AUC: 0.585, P = 0.009) and strain ratio by elastography (AUC: 0.522, P = 0.510). Moreover, the severity of skin tightness was reflected by the incremental changes of waveforms in the spectral diagrams. Conclusion: SAM was correlated with skin scores and sufficiently sensitive to detect subclinical disease. HSI can be used as a novel, non-invasive method for assessing skin changes in SSc.

This paper develops a new approach, called progressive band processing (PBP) of linear spectral unmixing (LSU) which can process data unmixing according to band sequential (BSQ) format. This new concept is quite different from band selection (BS) which must select bands from a fully collected band set based on a band optimization criterion. There are several advantages of using PBP-LSU over BS. In particular, it allows users to perform LSU using available bands without waiting for a complete collection of full bands. In doing so, an innovations information update equation is further derived and can process LSU as its band process is ongoing. To be more specific, LSU can be carried out by PBP by updating unmixed data recursively band by band in the same way that a Kalman filter does for updating data information in a recursive fashion. With such a recursion in nature, PBP is able to process bands of interest which may vary with different applications.