Synthesizing time-series wound prognosis factors from electronic medical records using generative adversarial networks

Age Encoded Adversarial Learning for Pediatric CT Segmentation

Organ segmentation from CT images is critical in the early diagnosis of diseases, progress monitoring, pre-operative planning, radiation therapy planning, and CT dose estimation. However, data limitation remains one of the main challenges in medical image segmentation tasks. This challenge is particularly huge in pediatric CT segmentation due to children's heightened sensitivity to radiation. In order to address this issue, we propose a novel segmentation framework with a built-in auxiliary classifier generative adversarial network (ACGAN) that conditions age, simultaneously generating additional features during training. The proposed conditional feature generation segmentation network (CFG-SegNet) was trained on a single loss function and used 2.5D segmentation batches. Our experiment was performed on a dataset with 359 subjects (180 male and 179 female) aged from 5 days to 16 years and a mean age of 7 years. CFG-SegNet achieved an average segmentation accuracy of 0.681 dice similarity coefficient (DSC) on the prostate, 0.619 DSC on the uterus, 0.912 DSC on the liver, and 0.832 DSC on the heart with four-fold cross-validation. We compared the segmentation accuracy of our proposed method with previously published U-Net results, and our network improved the segmentation accuracy by 2.7%, 2.6%, 2.8%, and 3.4% for the prostate, uterus, liver, and heart, respectively. The results indicate that our high-performing segmentation framework can more precisely segment organs when limited training images are available.

Deep learning classification of deep ultraviolet fluorescence images toward intra-operative margin assessment in breast cancer

Background

Breast-conserving surgery is aimed at removing all cancerous cells while minimizing the loss of healthy tissue. To ensure a balance between complete resection of cancer and preservation of healthy tissue, it is necessary to assess themargins of the removed specimen during the operation. Deep ultraviolet (DUV) fluorescence scanning microscopy provides rapid whole-surface imaging (WSI) of resected tissues with significant contrast between malignant and normal/benign tissue. Intra-operative margin assessment with DUV images would benefit from an automated breast cancer classification method.

Methods

Deep learning has shown promising results in breast cancer classification, but the limited DUV image dataset presents the challenge of overfitting to train a robust network. To overcome this challenge, the DUV-WSI images are split into small patches, and features are extracted using a pre-trained convolutional neural network-afterward, a gradient-boosting tree trains on these features for patch-level classification. An ensemble learning approach merges patch-level classification results and regional importance to determine the margin status. An explainable artificial intelligence method calculates the regional importance values.

Results

The proposed method's ability to determine the DUV WSI was high with 95% accuracy. The 100% sensitivity shows that the method can detect malignant cases efficiently. The method could also accurately localize areas that contain malignant or normal/benign tissue.

Conclusion

The proposed method outperforms the standard deep learning classification methods on the DUV breast surgical samples. The results suggest that it can be used to improve classification performance and identify cancerous regions more effectively.

ILSHIP: An interpretable and predictive model for hypothyroidism

Hypothyroidism is one of the common endocrine diseases, and its incidence is increasing year by year. Due to the insidious nature of this disease, it often leads to delayed treatment and even misdiagnosis. This paper proposes ILSHIP, an interpretable predictive model for hypothyroidism, to reduce its diagnostic complexity as well as improve the predictive performance and interpretability of existing models. First, the ILSHIP prediction model was built based on label encoding, missing value processing, feature selection, and data enhancement of the dataset. Second, the comprehensive performance of ILSHIP was compared with twelve existing related study models and eleven mainstream models, such as XGBoost and MLP. The experimental results showed that, based on the optimal hyperparameters the ILSHIP model can achieve 99.392%, 99.437%, 99.348%, 99.381%, and 99.960% in accuracy, recall, specificity, F1, and AUC, respectively. The accuracy of the ILSHIP model was about 0.7%-15.4% higher than the existing models. By introducing the SHAP framework into the ILSHIP model, important features affecting hypothyroidism such as thyroid stimulating hormone (TSH) and free thyroxine index (FTI) were also identified, and the influencing factors for different individuals were finally analyzed to provide a basis for medical personnel to monitor the condition.

Enhanced dataset synthesis using conditional generative adversarial networks

Biomedical data acquisition, and reaching sufficient samples of participants are difficult and time ans effort consuming processes. On the other hand, the success rates of computer aided diagnosis (CAD) algorithms are sample and feature space depended. In this paper, conditional generative adversarial network (CGAN) based enhanced feature generation is proposed to synthesize large sample datasets having higher class separability. Twenty five percent of five medical datasets are used to train CGAN, and the synthetic datasets with any sample size are evaluated and compared to originals. Thus, new datasets can be generated with the help of the CGAN model and lower sample collection. It helps physicians decreasing sample collection processes, and it increases accuracy rates of the CAD systems using generated enhanced data with enhanced feature vectors. The synthesized datasets are classified using nearest neighbor, radial basis function support vector machine and artificial neural network to analyze the effectiveness of the proposed CGAN model.

Deep Learning for Breast Cancer Classification of Deep Ultraviolet Fluorescence Images toward Intra-Operative Margin Assessment

Breast conserving surgery aims at the complete removal of malignant lesions while minimizing healthy tissue loss. To ensure the balance between complete resection of the cancer and conservation of healthy tissue, intra-operative margin assessment is necessary. Deep ultraviolet (DUV) fluorescence scanning microscope provides fast whole-surface-imaging (WSI) of excised tissue with contrast between malignant and normal tissues. Then, an automated breast cancer classification method on DUV images is required for intra-operative margin assessment. Deep learning shows the promising results in breast cancer classification, but limited DUV image dataset poses overfitting challenge to train the robust network. To tackle this challenge, we partition the DUV WSI image into small patches and extract pathological features for each patch from a pre-trained network using a transfer learning approach. We feed pathological features into a decision-tree-based classifier and fuse patch-level classification results based on regional importance to determine malignant or benign WSI. Experimental results on 60 DUV images show that our proposed method outperforms the standard deep learning classification in terms of improving the classification performance and identifying cancerous regions.

A review on contemporary nanomaterial-based therapeutics for the treatment of diabetic foot ulcers (DFUs) with special reference to the Indian scenario

Diabetes mellitus (DM) is a predominant chronic metabolic syndrome, resulting in various complications and high mortality associated with diabetic foot ulcers (DFUs). Approximately 15-30% of diabetic patients suffer from DFUs, which is expected to increase annually. The major challenges in treating DFUs are associated with wound infections, alterations to inflammatory responses, angiogenesis and lack of extracellular matrix (ECM) components. Furthermore, the lack of targeted therapy and efficient wound dressings for diabetic wounds often results in extended hospitalization and limb amputations. Hence, it is essential to develop and improve DFU-specific therapies. Nanomaterial-based innovative approaches have tremendous potential for preventing and treating wound infections of bacterial origin. They have greater benefits compared to traditional wound dressing approaches. In this approach, the physiochemical features of nanomaterials allow researchers to employ different methods for diabetic wound healing applications. In this review, the status and prevalence of diabetes mellitus (DM) and amputations due to DFUs in India, the pathophysiology of DFUs and their complications are discussed. Additionally, nanomaterial-based approaches such as the use of nanoemulsions, nanoparticles, nanoliposomes and nanofibers for the treatment of DFUs are studied. Besides, emerging therapeutics such as bioengineered skin substitutes and nanomaterial-based innovative approaches such as antibacterial hyperthermia therapy and gene therapy for the treatment of DFUs are highlighted. The present nanomaterial-based techniques provide a strong base for future therapeutic approaches for skin regeneration strategies in the treatment of diabetic wounds.