Cancer diagnosis using deep learning: A bibliographic review

In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements

Frequency analysis of functionally graded bi-layered cylindrical shells with ring support by galerkin technique

Buckling of vibrating cylindrical shells is an important aspect in aerospace and defense engineering. The proposed study is conducted to craving a method which is easier but still authentic for predicting the natural frequencies (Hz) of bi-layered cylindrical shells with ring support. The ring support is placed arbitrarily along the axis of the shell. It is assumed that the layers of the shell have a uniform thickness. Both layers are contrived independently by functionally graded technique having the constituents, stainless steel, and nickel. The material properties of the com- ponents of functionally graded layers are supervised by volume fraction power-law distribution and assumed to vary continuously and smoothly throughout the thickness of the layers. By interchanging the position of FGM constituents four kinds of cylindrical shells are formulated and its influence on frequency characteristics are analyzed. The expression for strain and curvature–displacement relationships are obtained by utilizing Love’s first approximation of linear thin shell theory. Simply supported end conditions are imposed on edges. For numerical approximations, the Galerkin approach is employed to formulate the frequency equation in the form of the eigenvalue problem. The variation in frequency for various shell parameters as; length, height, radius, the width of layers material constituents and the position of the ring supports position are discussed. Effectiveness, validity, and accuracy of the present methodology has proven by comparing the evaluated numerical results with the results available in the open literature

OFF PUMP CORONARY ARTERY SURGERY AND INTRAOPERATIVE SAFETY–EXPERIENCE AT AFIC/NIHD, RAWALPINDI

Background: Coronary Artery Bypass Grafting (CABG) with cardiopulmonary bypass (CPB) on one hand allows controlled haemodynamics with superior graft quality while on the other hand carries inherent risks of CPB which has renewed interest in Off-pump coronary artery bypass (OPCAB). Haemodynamic instability and intraoperative dysrythmias are major procedural complications of OPCAB, threatening conversion to emergency on-pump surgery. The purpose of this study was to compare intraoperative dysrythmias and inotropic use for haemodynamic stabilization during OPCAB surgery against conventional CABG. Methods: Consecutive CABG cases operated between 1st June 2003 and 31st May 2006 were included while conversions were excluded. Primary end points were analyzed using chi square and t test and values described in percentages, means and probability (p value). Results: Six hundred and eighty-four cases were divided in group-A (on-pump, n=574) and B (OPCAB, n=97). Conversion rate was 11.8%. Intraoperative dysrythmias (A, 3.5%, B, 15%, p<0.0001) and use of inotropic support was higher in group-B (A, 15.3%, B, 30.3%, p<0.0001). Actual mortality in group-B was higher than the predictive value (A, 3.8%, B, 3.6%, Predictive value 3–5 % and 0–3% respectively). Conclusions: OPCAB leads to higher frequency of dysrythmias and inotropic use intraoperatively, highlighting lower procedural safety over conventional CABG. Key words: OPCAB, intraoperative dysrythmias, inotropic us

Evaluation of prognostic risk models for postoperative pulmonary complications in adult patients undergoing major abdominal surgery: a systematic review and international external validation cohort study

Background Stratifying risk of postoperative pulmonary complications after major abdominal surgery allows clinicians to modify risk through targeted interventions and enhanced monitoring. In this study, we aimed to identify and validate prognostic models against a new consensus definition of postoperative pulmonary complications. Methods We did a systematic review and international external validation cohort study. The systematic review was done in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched MEDLINE and Embase on March 1, 2020, for articles published in English that reported on risk prediction models for postoperative pulmonary complications following abdominal surgery. External validation of existing models was done within a prospective international cohort study of adult patients (≥18 years) undergoing major abdominal surgery. Data were collected between Jan 1, 2019, and April 30, 2019, in the UK, Ireland, and Australia. Discriminative ability and prognostic accuracy summary statistics were compared between models for the 30-day postoperative pulmonary complication rate as defined by the Standardised Endpoints in Perioperative Medicine Core Outcome Measures in Perioperative and Anaesthetic Care (StEP-COMPAC). Model performance was compared using the area under the receiver operating characteristic curve (AUROCC). Findings In total, we identified 2903 records from our literature search; of which, 2514 (86·6%) unique records were screened, 121 (4·8%) of 2514 full texts were assessed for eligibility, and 29 unique prognostic models were identified. Nine (31·0%) of 29 models had score development reported only, 19 (65·5%) had undergone internal validation, and only four (13·8%) had been externally validated. Data to validate six eligible models were collected in the international external validation cohort study. Data from 11 591 patients were available, with an overall postoperative pulmonary complication rate of 7·8% (n=903). None of the six models showed good discrimination (defined as AUROCC ≥0·70) for identifying postoperative pulmonary complications, with the Assess Respiratory Risk in Surgical Patients in Catalonia score showing the best discrimination (AUROCC 0·700 [95% CI 0·683–0·717]). Interpretation In the pre-COVID-19 pandemic data, variability in the risk of pulmonary complications (StEP-COMPAC definition) following major abdominal surgery was poorly described by existing prognostication tools. To improve surgical safety during the COVID-19 pandemic recovery and beyond, novel risk stratification tools are required. Funding British Journal of Surgery Society