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Purpose To determine whether eyes with pathologic myopia can be identified and whether each type of myopic maculopathy lesion on fundus photographs can be diagnosed by deep learning (DL) algorithms. Design A DL algorithm was developed to recognize myopic maculopathy features and to categorize the myopic maculopathy automatically. Participants We examined 7020 fundus images from 4432 highly myopic eyes obtained from the Advanced Clinical Center for Myopia. Methods Deep learning (DL) algorithms were developed to recognize the key features of myopic maculopathy with 5176 fundus images. These algorithms were also used to develop a Meta-analysis for Pathologic Myopia (META-PM) study categorizing system (CS) by adding a specific processing layer. Models and the system were evaluated by 1844 fundus image. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to determine the performance of each DL algorithm. The rate of correct predictions was used to determine the performance of the META-PM study CS. Main Outcome Measures Four trained DL models were able to recognize the lesions of myopic maculopathy accurately with high sensitivity and specificity. The META-PM study CS also showed a high accuracy and was qualified to be used in a semiautomated way during screening for myopic maculopathy in highly myopic eyes. Results The sensitivity of the DL models was 84.44% for diffuse atrophy, 87.22% for patchy atrophy, 85.10% for macular atrophy, and 37.07% for choroidal neovascularization, and the AUC values were 0.970, 0.978, 0.982, and 0.881, respectively. The rate of total correct predictions from the META-PM study CS was 87.53%, with rates of 90.18%, 95.28%, 97.50%, and 91.14%, respectively, for each type of lesion. The META-PM study CS showed an overall rate of 92.08% in detecting pathologic myopia correctly, which was defined as having myopic maculopathy equal to or more serious than diffuse atrophy. Conclusions The novel DL models and system can achieve high sensitivity and specificity in identifying the different types of lesions of myopic maculopathy. These results will assist in the screening for pathologic myopia and subsequent protection of patients against low vision and blindness caused by myopic maculopathy.

The precise prediction of acute kidney injury (AKI) after nephrectomy for renal cell carcinoma (RCC) is an important issue because of its relationship with subsequent kidney dysfunction and high mortality. Herein we addressed whether machine learning (ML) algorithms could predict postoperative AKI risk better than conventional logistic regression (LR) models. A total of 4104 RCC patients who had undergone unilateral nephrectomy from January 2003 to December 2017 were reviewed. ML models such as support vector machine, random forest, extreme gradient boosting, and light gradient boosting machine (LightGBM) were developed, and their performance based on the area under the receiver operating characteristic curve, accuracy, and F1 score was compared with that of the LR-based scoring model. Postoperative AKI developed in 1167 patients (28.4%). All the ML models had higher performance index values than the LR-based scoring model. Among them, the LightGBM model had the highest value of 0.810 (0.783–0.837). The decision curve analysis demonstrated a greater net benefit of the ML models than the LR-based scoring model over all the ranges of threshold probabilities. The application of ML algorithms improves the predictability of AKI after nephrectomy for RCC, and these models perform better than conventional LR-based models.

Embryo selection within in vitro fertilization (IVF) is the process of evaluating qualities of fertilized oocytes (embryos) and selecting the best embryo(s) available within a patient cohort for subsequent transfer or cryopreservation. In recent years, artificial intelligence (AI) has been used extensively to improve and automate the embryo ranking and selection procedure by extracting relevant information from embryo microscopy images. The AI models are evaluated based on their ability to identify the embryo(s) with the highest chance(s) of achieving a successful pregnancy. Whether such evaluations should be based on ranking performance or pregnancy prediction, however, seems to divide studies. As such, a variety of performance metrics are reported, and comparisons between studies are often made on different outcomes and data foundations. Moreover, superiority of AI methods over manual human evaluation is often claimed based on retrospective data, without any mentions of potential bias. In this paper, we provide a technical view on some of the major topics that divide how current AI models are trained, evaluated and compared. We explain and discuss the most common evaluation metrics and relate them to the two separate evaluation objectives, ranking and prediction. We also discuss when and how to compare AI models across studies and explain in detail how a selection bias is inevitable when comparing AI models against current embryo selection practice in retrospective cohort studies.

PURPOSE: To assess whether utilization of a mathematical ranking algorithm for assistance with embryo selection improves clinical outcomes compared with traditional embryo selection via morphologic grading in single vitrified warmed euploid embryo transfers (euploid SETs). METHODS: A retrospective cohort study in a single, academic center from September 2016 to February 2020 was performed. A total of 4320 euploid SETs met inclusion criteria and were included in the study. Controls included all euploid SETs in which embryo selection was performed by a senior embryologist based on modified Gardner grading (traditional approach). Cases included euploid SETs in which embryo selection was performed using an automated algorithm-based approach (algorithm-based approach). Our primary outcome was implantation rate. Secondary outcomes included ongoing pregnancy/live birth rate and clinical loss rate. RESULTS: The implantation rate and ongoing pregnancy/live birth rate were significantly higher when using the algorithm-based approach compared with the traditional approach (65.3% vs 57.8%, p

Background Preoperative goals of care (GOC) and code status (CS) discussions are important in achieving an in-depth understanding of the patient's care goals in the setting of a serious illness, enabling the clinician to ensure patient autonomy and shared decision making. Past studies have shown that anesthesiologists are not formally trained in leading these discussions and may lack the necessary skill set. We created an innovative online video curriculum designed to teach these skills. This curriculum was compared to a traditional method of learning from reading the medical literature. Methods In this bi-institutional randomized controlled trial at 2 major academic medical centers, 60 anesthesiology trainees were randomized to receive the educational content in 1 of 2 formats: (1) the novel video curriculum (video group) or (2) journal articles (reading group). Thirty residents were assigned to the experimental video curriculum group, and 30 were assigned to the reading group. The content incorporated into the 2 formats focused on general preoperative evaluation of patients and communication strategies pertaining to GOC and CS discussions. Residents in both groups underwent a pre- and postintervention objective structured clinical examination (OSCE) with standardized patients. Both OSCEs were scored using the same 24-point rubric. Score changes between the 2 OSCEs were examined using linear regression, and interrater reliability was assessed using weighted Cohen's kappa. Results Residents receiving the video curriculum performed significantly better overall on the OSCE encounter, with a mean score of 4.19 compared to 3.79 in the reading group. The video curriculum group also demonstrated statistically significant increased scores on 8 of 24 rubric categories when compared to the reading group. Conclusions Our novel video curriculum led to significant increases in resident performance during simulated GOC discussions and modest increases during CS discussions. Further development and refinement of this curriculum are warranted.

Objectives This study aimed to establish a machine learning prediction model that can be used to predict bone metastasis (BM) in patients with newly diagnosed thyroid cancer (TC). Methods Demographic and clinicopathologic variables of TC patients in the Surveillance, Epidemiology, and End Results database from 2010 to 2016 were retrospectively analyzed. On this basis, we developed a random forest (RF) algorithm model based on machine‐learning. The area under receiver operating characteristic curve (AUC), accuracy score, recall rate, and specificity are used to evaluate and compare the prediction performance of the RF model and the other model. Results A total of 17,138 patients were included in the study, with 166 (0.97%) developed bone metastases. Grade, T stage, histology, race, sex, age, and N stage were the important prediction features of BM. The RF model has better predictive performance than the other model (AUC: 0.917, accuracy: 0.904, recall rate: 0.833, and specificity: 0.905). Conclusions The RF model constructed in this study could accurately predict bone metastases in TC patients, which may provide clinicians with more personalized clinical decision‐making recommendations. Machine learning technology has the potential to improve the development of BM prediction models in TC patients., We developed a random forest prediction model for bone metastases in thyroid cancer (TC) patients. This facilitates personalized diagnosis and refined clinical decision making for bone metastases in TC patients.

Artificial intelligence (AI) plays a major role in addressing novel coronavirus 2019 (COVID-19)-related issues and is also used in computer-aided synthesis planning (CASP). AI, including machine learning, is used by artificial neural networks such as deep neural networks and recurrent networks. AI has been used in activity predictions like physicochemical properties. Machine learning in de novo design explores the generation of fruitful, biologically active molecules toward expected or finished products. Several examples establish the strength of machine learning or AI in this field. AI techniques can significantly improve treatment consistency and decision making by developing useful algorithms. AI is helpful not only in the treatment of COVID-19-infected patients but also for their proper health monitoring. It can track the crisis of COVID-19 at different scales, such as medical, molecular, and epidemiological applications. It is also helpful to facilitate the research on this virus by analyzing the available data. AI can help in developing proper treatment regimens, prevention strategies, and drug and vaccine development. Combination with synthesis planning and ease of synthesis are feasible, and more and more automated drug discovery by computers is expected in the near future to eradicate the COVID-19 virus.

Dynamic decision-making was essential in the clinical care of surgical patients. Reinforcement learning (RL) algorithm is a computational method to find sequential optimal decisions among multiple suboptimal options. This review is aimed at introducing RL’s basic concepts, including three basic components: the state, the action, and the reward. Most medical studies using reinforcement learning methods were trained on a fixed observational dataset. This paper also reviews the literature of existing practical applications using reinforcement learning methods, which can be further categorized as a statistical RL study and a computational RL study. The review proposes several potential aspects where reinforcement learning can be applied in neurocritical and neurosurgical care. These include sequential treatment strategies of intracranial tumors and traumatic brain injury and intraoperative endoscope motion control. Several limitations of reinforcement learning are representations of basic components, the positivity violation, and validation methods.

At present, human health is threatened by many diseases, and lung cancer is one of the most dangerous tumors that threaten human life. In most developing countries, due to the large population and lack of medical resources, it is difficult for doctors to meet patients’ needs for medical treatment only by relying on the manual diagnosis. Based on massive medical information, the intelligent decision-making system has played a great role in assisting doctors in analyzing patients’ conditions, improving the accuracy of clinical diagnosis, and reducing the workload of medical staff. This article is based on the data of 8,920 nonsmall cell lung cancer patients collected by different medical systems in three hospitals in China. Based on the intelligent medical system, on the basis of the intelligent medical system, this paper constructs a nonsmall cell lung cancer staging auxiliary diagnosis model based on convolutional neural network (CNNSAD). CNNSAD converts patient medical records into word sequences, uses convolutional neural networks to extract semantic features from patient medical records, and combines dynamic sampling and transfer learning technology to construct a balanced data set. The experimental results show that the model is superior to other methods in terms of accuracy, recall, and precision. When the number of samples reaches 3000, the accuracy of the system will reach over 80%, which can effectively realize the auxiliary diagnosis of nonsmall cell lung cancer and combine dynamic sampling and migration learning techniques to train nonsmall cell lung cancer staging auxiliary diagnosis models, which can effectively achieve the auxiliary diagnosis of nonsmall cell lung cancer. The simulation results show that the model is better than the other methods in the experiment in terms of accuracy, recall, and precision.

Background and Objective Guideline development groups or other health care decision makers frequently encounter situations that require a simultaneous comparison of multiple interventions. This sometimes becomes apparent either when they identify questions of interest, before they formulate recommendations, or it may surface only when recommendations have already been formulated based on pairwise comparisons. Methods Using examples from the World Health Organization, the European Commission, and a professional society, we developed a flexible approach to developing recommendations when a multiple-intervention comparison (MC) is needed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) evidence to decision (EtD) frameworks. We iteratively refined this approach through user testing and then included a module in GRADE's official software GRADEpro to test the approach in two real and one theoretical guideline recommendations. Results We found the approach feasible and that all EtD criteria should be considered in an MC approach. We judged that guideline development groups and other decision makers will benefit from the availability of a network meta-analyses (NMA) of intervention effects to support decisions; however, NMA supports only one of many criteria, that is, the balance of health benefits and harms, and is therefore helpful, but not essential to the approach we propose. When similar but not identical comparators are used to address MC, challenges may arise with intransitivity and the relative rankings of interventions. Conclusion We successfully applied the MC approach and software module in generating recommendations across different scenarios and identified challenges. The MC approach allows guideline groups and other decision makers to transparently and critically assess multiple options for a given health question. Application of the approach by others may lead to refinement and allow for better understanding of its impact in developing recommendations and making choices.

Clinical decision-making in healthcare is already being influenced by predictions or recommendations made by data-driven machines. Numerous machine learning applications have appeared in the latest clinical literature, especially for outcome prediction models, with outcomes ranging from mortality and cardiac arrest to acute kidney injury and arrhythmia. In this review article, we summarize the state-of-the-art in related works covering data processing, inference, and model evaluation, in the context of outcome prediction models developed using data extracted from electronic health records. We also discuss limitations of prominent modeling assumptions and highlight opportunities for future research.

Estimates in a 1989 study indicated that physicians in the United States were unable to reach a diagnosis that accounted for their patient's symptoms in up to 90% of outpatient patient encounters. Many proponents of artificial intelligence (AI) see the current process of moving from clinical data gathering to medical diagnosis as being limited by human analytic capability and expect AI to be a valuable tool to refine this process. The use of AI fundamentally calls into question the extent to which uncertainty in medical decision making is tolerated. Uncertainty is perceived by some as fundamentally undesirable and thus, for them, optimal decision making should be based on minimizing uncertainty. However, uncertainty cannot be reduced to zero; thus, relative uncertainty can be used as a metric to weigh the likelihood of various diagnoses being correct and the appropriateness of treatments. Here, the authors make the argument, using as examples the experiences of 2 AI systems, IBM Watson on Jeopardy and Watson for Oncology, that medical decision making based on relative uncertainty provides a better lens for understanding the application of AI to medicine than one that minimizes uncertainty. This approach to uncertainty has significant implications for how health care leaders consider the benefits and trade-offs of AI-assisted and AI-driven decision tools and ultimately integrate AI into medical practice.

BACKGROUND: Computed Tomographic (CT) imaging procedures have been reported as the main source of radiation in diagnostic procedures compared to other modalities. To provide the optimal quality of CT images at the minimum radiation risk to the patient, periodic inspections and calibration tests for CT equipment are required. These tests involve a series of measurements that are time consuming and may require specific skills and highly-trained personnel. OBJECTIVE: This study aims to develop a new computational tool to estimate the dose of CT radiation outputs and assist in the calibration of CT scanners. It may also provide an educational resource by which radiological practitioners can learn the influence of technique factors on both patient radiation dose and the produced image quality. METHODS: The computational tool was developed using MATLAB in order to estimate the CT radiation dose parameters for different technique factors. The CT radiation dose parameters were estimated from the calibrated energy spectrum of the x-ray tube for a CT scanner. RESULTS: The estimated dose parameters and the measured values utilising an Adult CT Head Dose Phantom showed linear correlations for different tube voltages (80 kVp, 100 kVp, 120 kVp, and 140 kVp), with R2 nearly equal to 1 (0.99). The maximum differences between the estimated and measured CTDIvol were under 5 %. For 80 kVp and low tube currents (50 mA, 100 mA), the maximum differences were under 10%. CONCLUSIONS: The prototyped computational model provides a tool for the simulation of a machine-specific spectrum and CT dose parameters using a single dose measurement.

Objective To develop a collection of concept-relationship-concept tuples to formally represent patients’ care context data to inform electronic health record (EHR) development. Materials and Methods We reviewed semantic relationships reported in the literature and developed a manual annotation schema. We used the initial schema to annotate sentences extracted from narrative note sections of cardiology, urology, and ear, nose, and throat (ENT) notes. We audio recorded ENT visits and annotated their parsed transcripts. We combined the results of each annotation into a consolidated set of concept-relationship-concept tuples. We then compared the tuples used within and across the multiple data sources. Results We annotated a total of 626 sentences. Starting with 8 relationships from the literature, we annotated 182 sentences from 8 inpatient consult notes (initial set of tuples = 43). Next, we annotated 232 sentences from 10 outpatient visit notes (enhanced set of tuples = 75). Then, we annotated 212 sentences from transcripts of 5 outpatient visits (final set of tuples = 82). The tuples from the visit transcripts covered 103 (74%) concepts documented in the notes of their respective visits. There were 20 (24%) tuples used across all data sources, 10 (12%) used only in inpatient notes, 15 (18%) used only in visit notes, and 7 (9%) used only in the visit transcripts. Conclusions We produced a robust set of 82 tuples useful to represent patients’ care context data. We propose several applications of our tuples to improve EHR navigation, data entry, learning health systems, and decision support.

Objectives: Any attempt to introduce new data types in the entangled hospital infrastructure should help to unravel old knots without tangling new ones. Health data from a wide range of sources has become increasingly available. We witness an insatiable thirst for data in oncology as treatment paradigms are shifting to targeted molecular therapies. Methods: From nineteenth-century medical notes consisting entirely of narrative description to standardised forms recording physical examination and medical notes, we have nowadays moved to electronic health records (EHRs). All our analogue medical records are rendered as sequences of zeros and ones changing how we capture and share data. The challenge we face is to offload the analysis without entrusting a machine (or algorithms) to make major decisions about a diagnosis, a treatment, or a surgery, keeping the human oversight. Computers don’t have judgment, they lack context. Results: EHRs have become the latest addition to our toolset to look after patients. Moore’s law and general advances in computation have contributed to make EHRs a cornerstone of Molecular Tumour Boards, presenting a detailed and unique description of a tumour and treatment options. Conclusions: Precision oncology, as a systematic approach matching the most accurate and effective treatment to each individual cancer patient, based on a molecular profile, is already expanding to other disease areas.

Objective: To select the best papers that made original and high impact contributions in the area of human factors and organizational issues in biomedical informatics in 2019. Methods: A rigorous extraction process based on queries from Web of Science® and PubMed/Medline was conducted to identify the scientific contributions published in 2019 that address human factors and organizational issues in biomedical informatics. The screening of papers on titles and abstracts independently by the two editors led to a total of 30 papers. These papers were discussed for a selection of 15 finalist papers, which were then reviewed by the two editors and by three external reviewers from internationally renowned research teams. Results: The query process resulted in 626 papers that reveal interesting and rigorous methods and important studies in human factors that move the field forward, particularly in clinical informatics and emerging technologies such as brain-computer interfaces. This year three papers were clearly outstanding and help advance the field. They provide examples of applying existing frameworks together in novel and highly illuminating ways, showing the value of theory development in human factors. Conclusion: The selected papers make important contributions to human factors and organizational issues, expanding and deepening our knowledge of how to apply theory and applications of new technologies in health.

Background. The purpose of this review is to depict current research and impact of artificial intelligence/machine learning (AI/ML) algorithms on dialysis and kidney transplantation. Published studies were presented from two points of view: What medical aspects were covered? What AI/ML algorithms have been used? Methods. We searched four electronic databases or studies that used AI/ML in hemodialysis (HD), peritoneal dialysis (PD), and kidney transplantation (KT). Sixty-nine studies were split into three categories: AI/ML and HD, PD, and KT, respectively. We identified 43 trials in the first group, 8 in the second, and 18 in the third. Then, studies were classified according to the type of algorithm. Results. AI and HD trials covered: (a) dialysis service management, (b) dialysis procedure, (c) anemia management, (d) hormonal/dietary issues, and (e) arteriovenous fistula assessment. PD studies were divided into (a) peritoneal technique issues, (b) infections, and (c) cardiovascular event prediction. AI in transplantation studies were allocated into (a) management systems (ML used as pretransplant organ-matching tools), (b) predicting graft rejection, (c) tacrolimus therapy modulation, and (d) dietary issues. Conclusions. Although guidelines are reluctant to recommend AI implementation in daily practice, there is plenty of evidence that AI/ML algorithms can predict better than nephrologists: volumes, Kt/V, and hypotension or cardiovascular events during dialysis. Altogether, these trials report a robust impact of AI/ML on quality of life and survival in G5D/T patients. In the coming years, one would probably witness the emergence of AI/ML devices that facilitate the management of dialysis patients, thus increasing the quality of life and survival.

Indication for pancreatoduodenectomy for pancreatic cancer can be challenging. Wrong decisions in indication processes lead to significant health impairments. Computerized decision support systems can take over parts of decision-making processes, making them more accurate. MEBDAS® is a decision-supporting software that predicts outcomes of proposed treatments.to determine the decision concordance between MEBDAS® and multidisciplinary tumour board (MTB) and the impact of MEBDAS® on in-hospital outcome at different indication thresholds.126 patients with pancreatoduodenectomy from a high-volume university hospital were included. Outcome indicators were in-hospital mortality, Comprehensive Complication Index (CCI®), therapy-related loss of "Quality-Adjusted-Life-Day" (QALD-loss) and prognostic gain of treatment-related "Quality-Adjusted-Life-Year" (QALY-gain).The concordance of decisions was 94.4% at the indication threshold of 0. By raising the indication threshold to 1 year, the concordance decreased to 0%, the in-hospital-mortality dropped from 2.52% to 0%, the CCI® decreased from 26.47 to 13.90, the therapy-related QALD-loss declined from 21.53 to 16.22 days and the prognostic QALY-gain increased from 0.374 to 0.906 years. At IT = 0.250 years, the concordance was 61.11% and differences between MTB and MEBDAS®-group were highly significant (p 0.001) for all outcome parameters: mortality (3.97% vs. 1.30%), CCI® (28.96 vs. 18.29), therapy-related QALD-loss (24.41 vs. 15.19 days) and QALY-gain (0.351 vs. 0.501 years).MEBDAS® decisions are superior to those of MTB in terms of in-hospital-outcome. The inclusion of MEBDAS® in decision procedure makes the indication more accurate and reduces morbidity and mortality. In addition, MEBDAS® can increase patients' competence by involving them in decision-making process.

BACKGROUND: Cochlear morphology and cochlear duct length (CDL) play important roles in the selection of appropriate electrodes. Cochlear parameters such as diameter (A value) and width (B value) are used as inputs for calculating the CDL. Current measurements of these parameters are inefficient and time consuming. Recently developed otological planning software (OTOPLAN) allows surgeons to directly measure these parameters and then automatically calculate the CDL. OBJECTIVES: The primary objective was to validate this new software for measuring the cochlear parameters and CDL. The secondary aim was to investigate the correlation between each cochlear parameter with the calculated CDL. DESIGN: Retrospective. SETTINGS: Ear specialist hospital. PATIENTS AND METHODS: The measurement of cochlear diameter (A value) was chosen as the validation parameter. To do this, the A value was measured by a neurotologist on the new OTOPLAN planning software and was validated to the one measured on the currently used DICOM viewer. Upon the validation of the OTOPLAN software, the other two cochlear parameters, namely width (B value) and height (H value) were measured, and CDL was automatically calculated. Finally, the correlation of all parameters with the CDL was statistically analyzed. MAIN OUTCOME MEASURES: Validation of OTOPLAN and CDL estimation. SAMPLE SIZE: 88 ears. RESULTS: There was no significant difference between the A-value measured on the DICOM viewing software and that on the new planning software by the two independent neurotologists (P=.27). Both A-and B-values showed a high positive correlation to the CDL. However, the B-value showed a stronger correlation to the CDL than the A-value (r=0.63 for A, and r=0.96 for B). CONCLUSION: The direct measurement of cochlea parameters and automatic calculation of the CDL could improve the efficiency of clinical workflow and make otology surgeons more independent. Moreover, the cochlear width (B) has a strong correlation to the CDL. Thus, we suggest using the combination of A and B to accurately estimate the CDL rather than using only one. LIMITATIONS: Single center and small sample size. CONFLICT OF INTEREST: None. No relationship with manufacturers.

This paper uses computer technology combined with regression model to analyze the risk factors of children with sepsis, determine the relevant factors and establish a corresponding early warning model of sepsis, and then verify the clinical application value of the regression model. The paper collected severe infections and sepsis in children who came to our hospital from 2014 to 2018, including 129 patients with infection and 86 patients with sepsis. The general conditions, clinical symptoms, laboratory tests and other factors were used. Analysis, to identify the risk of infection development into sepsis, and use Logistic regression model combined with computer technology to construct an early warning model of sepsis. The experimental results show that early warning of sepsis is closely related to skin spots, platelets, procalcitonin, creatinine and international normalized ratio. The experiment demonstrates that the early warning model has higher sensitivity and specificity, and has higher accuracy for predicting whether infection develops into sepsis in advance, and has certain clinical value. Keywords: Logistic regression analysis, Computer technology, Early warning model for children with sepsis

Digitalized hand-drawn pattern is a noninvasive and reproducible assistive manner to obtain hand actions and motions for evaluating functional tremors and upper-limb movement disorders. In this study, spirals and straight lines in polar coordinates are used to extract polar expression features such as the key parameters deviation (cm) and accumulation angle (rad). These parameters are quantitative manner to scale the variations of functional tremors in normal control subjects and patients with Parkinson's disease (PD) and essential tremor (ET). However, difficulty arises in using nonlinear polar expression features in the two-dimensional feature space to separate normal control subjects from those with PD and ET. To solve the nonlinear separable classification problem, hash transformation is used to map polar expression features to a high-dimensional space using hash weighing function and modulo operation. Then, a machine learning method, such as the generalized regression neural network (GRNN), is implemented to train a decision-making classifier using the particle swarm optimization (PSO) algorithm for possible class assessment. With the enrolled data from 50 subjects, the fivefold cross validation, mean true positive, mean true negative, and mean hit rates of 98.93%, 98.96%, and 98.93%, respectively, are obtained to quantify the performance of the proposed decision-making classifier to identify normal controls and subjects with PD or ET. The experimental results indicate that the proposed screening model can improve the accuracy rate compared with the conventional machine learning classifier.

Background Intertrochanteric femoral fractures are prevalent among the elderly, and usually demands surgical treatments. Proximal femoral nail antirotation Asian version (PFNA-II) is widely used for intertrochanteric fracture treatment. The computer-assisted preoperative planning (CAPP) system has the potential to reduce the difficulty of PFNA-II in the treatment of intertrochanteric fractures. The aim of the study was to investigate and compare the learning curves of PFNA-II treatment with CAPP and conventional preoperational planning methods for intertrochanteric femoral fractures. Methods A total of 125 patients with intertrochanteric fracture who were treated with PFNA-II between March 2012 and June 2015 were retrospectively analyzed. Patients who underwent surgery with CAPP procedure by a junior surgeon were regarded as group A (n = 53); patients who underwent the conventional surgery by another junior surgeon were regarded as group B (n = 72). Each group was divided into three subgroups (case 1–20, case 21–40, case 41–53 or case 41–72). Results The average operation time of group A was 45.00(42.00, 50.00) minutes, and in group B was 55.00 (50.00, 60.00) minutes (P P P Conclusion Compared with conventional preoperative planning methods, CAPP system significantly reduced operation time, radiation frequency and blood loss, thus reshaped the learning curve of PFNA-II treatment with lower learning difficulty. Trial registration researchregistry4770. Registered 25 March 2019.

While the world continues to grapple with the devastating effects of the SARS-nCoV-2 virus, different scientific groups, including researchers from different parts of the world, are trying to collaborate to discover solutions to prevent the spread of the COVID-19 virus permanently. Henceforth, the current study envisions the analysis of predictive models that employ machine learning techniques and mathematical modeling to mitigate the spread of COVID-19. A systematic literature review (SLR) has been conducted, wherein a search into different databases, viz., PubMed and IEEE Explore, fetched 1178 records initially. From an initial of 1178 records, only 50 articles were analyzed completely. Around (64%) of the studies employed data-driven mathematical models, whereas only (26%) used machine learning models. Hybrid and ARIMA models constituted about (5%) and (3%) of the selected articles. Various Quality Evaluation Metrics (QEM), including accuracy, precision, specificity, sensitivity, Brier-score, F1-score, RMSE, AUC, and prediction and validation cohort, were used to gauge the effectiveness of the studied models. The study also considered the impact of Pfizer-BioNTech (BNT162b2), AstraZeneca (ChAd0x1), and Moderna (mRNA-1273) on Beta (B.1.1.7) and Delta (B.1.617.2) viral variants and the impact of administering booster doses given the evolution of viral variants of the virus.

Epidermal growth factor receptor (EGFR) mutation testing has several limitations. Therefore, we built predictive models to determine the EGFR mutation status of patients and guide therapeutic decision-making. We collected data from 320 patients with lung carcinoma, including sex, age, smoking history, serum tumour marker levels, maximum standardized uptake value, pathological results, computed tomography images, and EGFR mutation status. Artificial neural network (ANN) models based on multiple clinical characteristics were proposed to predict EGFR mutation status. A training set (n = 200) was used to develop predictive models of the EGFR mutation status (Model 1: area under the receiver operating characteristic curve [AUROC] = 0.910, 95% CI 0.861–0.945; Model 2: AUROC = 0.859, 95% CI 0.803–0.904; Model 3: AUROC = 0.711, 95% CI 0.643–0.773). A testing set (n = 50) and temporal validation data set (n = 70) were used to evaluate the generalisation performance of the established models (testing set: Model 1, AUROC = 0.845, 95% CI 0.715–0.932; Model 2, AUROC = 0.882, 95% CI 0.759–0.956; Model 3, AUROC = 0.817, 95% CI 0.682–0.912; temporal validation dataset: Model 1, AUROC = 0.909, 95% CI 0.816–0.964; Model 2, AUROC = 0.855, 95% CI 0.751–0.928; Model 3, AUROC = 0.831, 95% CI 0.723–0.910). The predictive abilities of the three ANN models were superior to that of a previous logistic regression model (P

The design of efficient combination therapies is a difficult key challenge in the treatment of complex diseases such as cancers. The large heterogeneity of cancers and the large number of available drugs renders exhaustive in vivo or even in vitro investigation of possible treatments impractical. In recent years, sophisticated mechanistic, ordinary differential equation-based pathways models that can predict treatment responses at a molecular level have been developed. However, surprisingly little effort has been put into leveraging these models to find novel therapies. In this paper we use for the first time, to our knowledge, a large-scale state-of-the-art pan-cancer signaling pathway model to identify candidates for novel combination therapies to treat individual cancer cell lines from various tissues (e.g., minimizing proliferation while keeping dosage low to avoid adverse side effects) and populations of heterogeneous cancer cell lines (e.g., minimizing the maximum or average proliferation across the cell lines while keeping dosage low). We also show how our method can be used to optimize the drug combinations used in sequential treatment plans—that is, optimized sequences of potentially different drug combinations—providing additional benefits. In order to solve the treatment optimization problems, we combine the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm with a significantly more scalable sampling scheme for truncated Gaussian distributions, based on a Hamiltonian Monte-Carlo method. These optimization techniques are independent of the signaling pathway model, and can thus be adapted to find treatment candidates for other complex diseases than cancers as well, as long as a suitable predictive model is available., Author summary Combination therapies are a promising approach to counter complex diseases such as cancers. Two key difficulties in the design of effective cancer combination therapies are the large number of available drugs and the heterogeneity of cancers which render exhaustive laboratory studies impractical. In recent years, sophisticated signaling pathway models that can predict responses to combination treatments at a molecular level have been developed. This motivates the question of how one can leverage mechanistic models to identify candidates for novel combination treatments. In this paper we propose a combination treatment optimization framework which employs a large-scale pan-cancer pathway model. We formulate treatment optimization problems for single cell lines and heterogeneous populations of cancer cells. We further investigate sequential treatment plans and combine an existing evolutionary algorithm with an efficient Hamiltonian Monte-Carlo based sampling scheme. During extensive simulation studies our approach identified combination therapies which are predicted to be more effective than conventional treatments. We hope that one day in silico experiments will be used to identify a small set of promising treatment candidates which can then form a starting point for laboratory studies, allowing for an efficient use of limited resources and accelerated discovery of effective therapies.

There is a growing interest in the application of artificial intelligence (AI) to orthopaedic surgery. This review aims to identify and characterise research in this field, in order to understand the extent, range and nature of this work, and act as springboard to stimulate future studies. A scoping review, a form of structured evidence synthesis, was conducted to summarise the use of AI in orthopaedics. A literature search (1946–2019) identified 222 studies eligible for inclusion. These studies were predominantly small and retrospective. There has been significant growth in the number of papers published in the last three years, mainly from the USA (37%). The majority of research used AI for image interpretation (45%) or as a clinical decision tool (25%). Spine (43%), knee (23%) and hip (14%) were the regions of the body most commonly studied. The application of artificial intelligence to orthopaedics is growing. However, the scope of its use so far remains limited, both in terms of its possible clinical applications, and the sub-specialty areas of the body which have been studied. A standardized method of reporting AI studies would allow direct assessment and comparison. Prospective studies are required to validate AI tools for clinical use.