A Novel Machine Learning Approach for Severity Classification of Diabetic Foot Complications Using Thermogram Images.

Diabetes mellitus (DM) is one of the most prevalent diseases in the world, and is correlated to a high index of mortality. One of its major complications is diabetic foot, leading to plantar ulcers, amputation, and death. Several studies report that a thermogram helps to detect changes in the plantar temperature of the foot, which may lead to a higher risk of ulceration. However, in diabetic patients, the distribution of plantar temperature does not follow a standard pattern, thereby making it difficult to quantify the changes. The abnormal temperature distribution in infrared (IR) foot thermogram images can be used for the early detection of diabetic foot before ulceration to avoid complications. There is no machine learning-based technique reported in the literature to classify these thermograms based on the severity of diabetic foot complications. This paper uses an available labeled diabetic thermogram dataset and uses the k-mean clustering technique to cluster the severity risk of diabetic foot ulcers using an unsupervised approach. Using the plantar foot temperature, the new clustered dataset is verified by expert medical doctors in terms of risk for the development of foot ulcers. The newly labeled dataset is then investigated in terms of robustness to be classified by any machine learning network. Classical machine learning algorithms with feature engineering and a convolutional neural network (CNN) with image-enhancement techniques are investigated to provide the best-performing network in classifying thermograms based on severity. It is found that the popular VGG 19 CNN model shows an accuracy, precision, sensitivity, F1-score, and specificity of 95.08%, 95.08%, 95.09%, 95.08%, and 97.2%, respectively, in the stratification of severity. A stacking classifier is proposed using extracted features of the thermogram, which is created using the trained gradient boost classifier, XGBoost classifier, and random forest classifier. This provides a comparable performance of 94.47%, 94.45%, 94.47%, 94.43%, and 93.25% for accuracy, precision, sensitivity, F1-score, and specificity, respectively.

Successful Surgical Management of Unusual Gallbladder Anatomy Through Laparoscopic Cholecystectomy of Ectopic Gallbladder.

Abnormalities in the position of the gallbladder are not encountered commonly such as the ectopic location. We present a case of laparoscopic cholecystectomy for gallbladder that was found in an ectopic position. The surgical procedure can be difficult in some cases of acute cholecystitis and ectopic position of the gallbladder may add to complexities of the procedure due to abnormal location or anatomical variants of the biliary tree. Preoperative identification of ectopic gallbladder may aid in planning and performing a safe surgical procedure.

A machine learning model for early detection of diabetic foot using thermogram images.

Diabetes foot ulceration (DFU) and amputation are a cause of significant morbidity. The prevention of DFU may be achieved by the identification of patients at risk of DFU and the institution of preventative measures through education and offloading. Several studies have reported that thermogram images may help to detect an increase in plantar temperature prior to DFU. However, the distribution of plantar temperature may be heterogeneous, making it difficult to quantify and utilize to predict outcomes. We have compared a machine learning-based scoring technique with feature selection and optimization techniques and learning classifiers to several state-of-the-art Convolutional Neural Networks (CNNs) on foot thermogram images and propose a robust solution to identify the diabetic foot. A comparatively shallow CNN model, MobilenetV2 achieved an F1 score of ∼95% for a two-feet thermogram image-based classification and the AdaBoost Classifier used 10 features and achieved an F1 score of 97%. A comparison of the inference time for the best-performing networks confirmed that the proposed algorithm can be deployed as a smartphone application to allow the user to monitor the progression of the DFU in a home setting.

Development of nitric oxide releasing visible light crosslinked gelatin methacrylate hydrogel for rapid closure of diabetic wounds.

Management of non-healing and slow to heal diabetic wounds is a major concern in healthcare across the world. Numerous techniques have been investigated to solve the issue of delayed wound healing, though, mostly unable to promote complete healing of diabetic wounds due to the lack of proper cell proliferation, poor cell-cell communication, and higher chances of wound infections. These challenges can be minimized by using hydrogel based wound healing patches loaded with bioactive agents. Gelatin methacrylate (GelMA) has been proven to be a highly cell friendly, cell adhesive, and inexpensive biopolymer for various tissue engineering and wound healing applications. In this study, S-Nitroso-N-acetylpenicillamine (SNAP), a nitric oxide (NO) donor, was incorporated in a highly porous GelMA hydrogel patch to improve cell proliferation, facilitate rapid cell migration, and enhance diabetic wound healing. We adopted a visible light crosslinking method to fabricate this highly porous biodegradable but relatively stable patch. Developed patches were characterized for morphology, NO release, cell proliferation and migration, and diabetic wound healing in a rat model. The obtained results indicate that SNAP loaded visible light crosslinked GelMA hydrogel patches can be highly effective in promoting diabetic wound healing.

Platelet to Lymphocyte Ratio Associated with Prolonged Hospital Length of Stay Postpeptic Ulcer Perforation Repair: An Observational Descriptive Analysis.

BACKGROUND: The predictive role of platelet to lymphocyte ratio (P/LR) in patients with perforated peptic ulcer (PPU) is not well-studied. We aimed to investigate the association between the P/LR ratio and the hospital length of stay (HLOS) for surgically treated PPU. METHOD: This is a retrospective observational study for surgically treated adult cases of PPU at Hamad Medical Corporation during the period from January 2012 to August 2017. Patients were categorized into two groups based on their HLOS (I week). The receiver operating characteristic (ROC) curve was plotted to determine the cutoff value for lymphocyte count, neutrophil to lymphocyte ratio, and P/LR ratio for predicting the prolonged hospitalization. RESULTS: One hundred and fifty-two patients were included in the study. The majority were young males. The mean age was 38.3 ± 12.7 years. Perforated duodenal ulcer (139 patients) exceeded perforated gastric ulcer (13 patients). The HLOS > 1 week was observed in 14.5% of cases. Older age (p = 0.01), higher preoperative WBC (p = 0.03), lower lymphocyte count (p = 0.01), and higher P/LR ratio (p = 0.005) were evident in the HLOS > 1 week group. The optimal cutoff value of P/LR was 311.2 with AUC 0.702 and negative predictive value of 93% for the prediction of prolonged hospitalization. Two patients died with a mean P/LR ratio of 640.8 ± 135.5 vs. 336.6 ± 258.9 in the survivors. CONCLUSION: High preoperative P/LR value predicts prolonged HLOS in patients with repaired perforated peptic ulcer. Further larger multicenter studies are needed to support the study findings.

Growth factor loaded in situ photocrosslinkable poly(3-hydroxybutyrate-co-3-hydroxyvalerate)/gelatin methacryloyl hybrid patch for diabetic wound healing.

Management of chronic diabetic ulcers remains as a major challenge in healthcare which requires extensive multidisciplinary approaches to ensure wound protection, management of excess wound exudates and promoting healing. Developing wound healing patches that can act as a protective barrier and support healing is highly needed to manage chronic diabetic ulcers. In order to boost the wound healing potential of patch material, bioactive agents such as growth factors can be used. Porous membranes made of nanofibers generated using electrospinning have potential for application as wound coverage matrices. However, electrospun membranes produced from several biodegradable polymers are hydrophobic and cannot manage the excess exudates produced by chronic wounds. Gelatin-methacryloyl (GelMA) hydrogels absorb excess exudates and provide an optimal biological environment for the healing wound. Epidermal growth factor (EGF) promotes cell migration, angiogenesis and overall wound healing. Poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) membranes provide microbial, thermal and mechanical barrier properties to the wound healing patch. Herein, we developed a biodegradable polymeric patch based on the combination of mechanically stable electrospun PHBV, GelMA hydrogel and EGF for promoting diabetic wound healing. In vitro and in vivo studies were carried out to evaluate the effect of developed patches on cell proliferation, cell migration, angiogenesis and wound healing. Our results showed that EGF loaded patches can promote the migration and proliferation of multiple types of cells (keratinocytes, fibroblasts and endothelial cells) and enhance angiogenesis. In situ development of the patch and subsequent in vivo wound healing study in diabetic rats showed that EGF loaded patches provide rapid healing compared to control wounds. Interestingly, 100 ng EGF per cm2 of the patches was enough to provide favourable cellular response, angiogenesis and rapid diabetic wound healing. Overall results indicate that EGF loaded PHBV-GelMA hybrid patch could be a promising approach to promote diabetic wound healing.

Assessment of PULP score in predicting 30-day perforated duodenal ulcer morbidity, and comparison of its performance with Boey and ASA, a retrospective study.

BACKGROUND: /aim: Scores commonly employed to risk stratify perforated peptic ulcer patients include ASA (American Society of Anesthesiologists), Boey and peptic ulcer perforation score (PULP). However, few studies assessed and compared the accuracy indices of these three scores in predicting post PPU repair 30-day morbidity. We assessed accuracy indices of PULP, and compared them to Boey and ASA in predicting post perforated duodenal (PDU) ulcer repair 30-day morbidity. METHODS: Retrospective chart review of all PDU patients (perforated duodenal ulcers only) at the largest two hospitals in Qatar (N = 152). Data included demographic, clinical, laboratory, operative, and post repair 30-day morbidity. Area under the Curve (AUC), sensitivity and specificity were computed for each of the 3 scores. Multivariate logistic regression assessed the accuracy indices of each score. RESULTS: All patients were males (M age 37.41 years). Post PDU repair 30-day morbidity was 10.5% (16 morbidities). Older age, higher ASA (≥3), Boey (≥1) or PULP (≥8) scores, shock on admission and preoperative comorbidities; and conversely, lower hemoglobin and albumin were all positively significantly associated with higher post PDU 30-day morbidity. PULP displayed the largest AUC (72%), and was the only score to significantly predict 30-day morbidity. The current study is the first to report the sensitivity and specificity of these three scores for post PDU repair 30-day morbidity; and first to assess accuracy indices for PULP in predicting post PDU repair 30-day morbidity. CONCLUSION: PULP score had the largest AUC and was the only score to significantly predict post PDU repair 30-day morbidity.