CORR Synthesis: When Should the Orthopaedic Surgeon Use Artificial Intelligence, Machine Learning, and Deep Learning?

Artificial intelligence: Friend or foe?

Artificial intelligence in COPD: Possible applications and future prospects.

Chronic liver disease and cirrhosis are persistent global health threats, ranking among the top causes of death. Despite medical advancements, their mortality rates have remained stagnant for decades. Existing scoring systems such as Child-Turcotte-Pugh and Mayo End-Stage Liver Disease have limitations, prompting the exploration of more accurate predictive methods using artificial intelligence and machine learning (ML). We retrospectively reviewed the data of all adult patients with acute decompensated liver cirrhosis admitted to a tertiary hospital during 2015-2021. The dataset underwent preprocessing to handle missing values and standardize continuous features. Traditional ML and deep learning algorithms were applied to build a 28-day mortality prediction model. The subjects were 173 cirrhosis patients, whose medical records were examined. We developed and evaluated multiple models for 28-day mortality prediction. Among traditional ML algorithms, logistic regression outperformed was achieving an accuracy of 82.9%, precision of 55.6%, recall of 71.4%, and an F1-score of 0.625. Naive Bayes and Random Forest models also performed well, both achieving the same accuracy (82.9%) and precision (54.5%). The deep learning models (multilayer artificial neural network, recurrent neural network, and Long Short-Term Memory) exhibited mixed results, with the multilayer artificial neural network achieving an accuracy of 74.3% but lower precision and recall. The feature importance analysis identified key predictability contributors, including admission in the intensive care unit (importance: 0.112), use of mechanical ventilation (importance: 0.095), and mean arterial pressure (importance: 0.073). Our study demonstrates the potential of ML in predicting 28-day mortality following hospitalization with acute decompensation of liver cirrhosis. Logistic Regression, Naive Bayes, and Random Forest models proved effective, while deep learning models exhibited variable performance. These models can serve as useful tools for risk stratification and timely intervention. Implementing these models in clinical practice has the potential to improve patient outcomes and resource allocation.

Comprehensive Study for Breast Cancer Using Deep Learning and Traditional Machine Learning

The review presents a comprehensive analysis of the latest advances in machine learning (ML), artificial neural networks (ANN), and deep learning (DL) in urologic oncology. As part of the study, the Russian and foreign scientific literature was ranked based on PubMed, MEDLINE, E-library, CYBERLENINKA, etc. The data related to the use of ML, ANN, and DL in the diagnosis and treatment of prostate cancer (PCa), bladder cancer (BC), testicular cancer, and kidney cancer was collected. Most often, ANN and ML in PCa were used for early diagnosis, prognosis, and personalized systemic treatment strategy development. ANN and DL models were trained with clinical parameters, NGS-sequencing results, Gleason scores, and digitized radiological, and histological images. Radiomics was also used to diagnose PCa, followed by analysis of special image texture features on a digital slide. In metastatic castration-resistant PCa, artificial intelligence (AI) algorithms were used to predict the response to docetaxel treatment. The prospects of using AI for tumor imaging during radical prostatectomy and when performing robot-assisted kidney resection were also addressed. A diagnostic approach for testicular malignancies based on computed tomography data is proposed using ML. Neuro-fuzzy modeling and ANN were used to diagnose BC. The algorithms were based on molecular biomarkers, including gene expression and methylation. The ML method based on images of cells obtained from urine samples of patients diagnosed with BC showed a diagnostic accuracy of 94%. DL in BC was used for accurate tumor typing based on their response to chemotherapy. Based on the results of deep machine learning, the molecular subtype of BC samples was predicted using histological examination. ML and DL algorithms for diagnosis, differential diagnosis, and prediction of recurrence and survival in kidney cancer were trained on CT texture analysis, genetic mutations, and Fuhrman nuclear grade. In addition to diagnosis, AI is used to optimize the treatment strategy for kidney cancer. In all cases, the ML, ANN, and DL algorithms improved the accuracy of diagnosis, survival assessment, and the effectiveness of pharmacological and surgical treatment of urologic malignancies.

Machine Learning Reimagined: The Promise of Interpretability to Combat Bias.

Next basket recommendation is a critical task in market basket data analysis. It is particularly important in grocery shopping, where grocery lists are an essential part of shopping habits of many customers. In this work, we first present a new grocery Recommender System available on the MyGroceryTour platform. Our online system uses different traditional machine learning (ML) and deep learning (DL) algorithms, and provides recommendations to users in a real-time manner. It aims to help Canadian customers create their personalized intelligent weekly grocery lists based on their individual purchase histories, weekly specials offered in local stores, and product cost and availability information. We perform clustering analysis to partition given customer profiles into four non-overlapping clusters according to their grocery shopping habits. Then, we conduct computational experiments to compare several traditional ML algorithms and our new DL algorithm based on the use of a gated recurrent unit (GRU)-based recurrent neural network (RNN) architecture. Our DL algorithm can be viewed as an extension of DREAM (Dynamic REcurrent bAsket Model) adapted to multi-class (i.e. multi-store) classification, since a given user can purchase recommended products in different grocery stores in which these products are available. Among traditional ML algorithms, the highest average F-score of 0.516 for the considered data set of 831 customers was obtained using Random Forest, whereas our proposed DL algorithm yielded the average F-score of 0.559 for this data set. The main advantage of the presented Recommender System is that our intelligent recommendation is personalized, since a separate traditional ML or DL model is built for each customer considered. Such a personalized approach allows us to outperform the prediction results provided by general state-of-the-art DL models.

Artificial intelligence and treatment algorithms in spine surgery

Perioperative hypotension 2021: a contrarian view

Digital image processing has witnessed a significant transformation, owing to the adoption of deep learning (DL) algorithms, which have proven to be vastly superior to conventional methods for crop detection. These DL algorithms have recently found successful applications across various domains, translating input data, such as images of afflicted plants, into valuable insights, like the identification of specific crop diseases. This innovation has spurred the development of cutting-edge techniques for early detection and diagnosis of crop diseases, leveraging tools such as convolutional neural networks (CNN), K-nearest neighbour (KNN), support vector machines (SVM), and artificial neural networks (ANN). This paper offers an all-encompassing exploration of the contemporary literature on methods for diagnosing, categorizing, and gauging the severity of crop diseases. The review examines the performance analysis of the latest machine learning (ML) and DL techniques outlined in these studies. It also scrutinizes the methodologies and datasets and outlines the prevalent recommendations and identified gaps within different research investigations. As a conclusion, the review offers insights into potential solutions and outlines the direction for future research in this field. The review underscores that while most studies have concentrated on traditional ML algorithms and CNN, there has been a noticeable dearth of focus on emerging DL algorithms like capsule neural networks and vision transformers. Furthermore, it sheds light on the fact that several datasets employed for training and evaluating DL models have been tailored to suit specific crop types, emphasizing the pressing need for a comprehensive and expansive image dataset encompassing a wider array of crop varieties. Moreover, the survey draws attention to the prevailing trend where the majority of research endeavours have concentrated on individual plant diseases, ML, or DL algorithms. In light of this, it advocates for the development of a unified framework that harnesses an ensemble of ML and DL algorithms to address the complexities of multiple plant diseases effectively.

SummaryThe recent sale of an artificial intelligence (AI)-generated portrait for $432,000 at Christie's art auction has raised questions about how credit and responsibility should be allocated to individuals involved and how the anthropomorphic perception of the AI system contributed to the artwork's success. Here, we identify natural heterogeneity in the extent to which different people perceive AI as anthropomorphic. We find that differences in the perception of AI anthropomorphicity are associated with different allocations of responsibility to the AI system and credit to different stakeholders involved in art production. We then show that perceptions of AI anthropomorphicity can be manipulated by changing the language used to talk about AI—as a tool versus agent—with consequences for artists and AI practitioners. Our findings shed light on what is at stake when we anthropomorphize AI systems and offer an empirical lens to reason about how to allocate credit and responsibility to human stakeholders.

Artificial Intelligence-related Literature in Transplantation: A Practical Guide.

Teaching an old dog new tricks: three-dimensional visual spatialisation of viscoelastic testing and artificial intelligence.

Plants meet machines: Prospects in machine learning for plant biology

Acute pancreatitis (AP) is a prevalent pathological condition of abdomen characterized by sudden onset, high incidence and complex progression. Timely assessment of AP severity is crucial for informing intervention decisions so as to delay deterioration and reduce mortality rates. Existing AP-related scoring systems can only assess current condition of patients and utilize only a single type of clinical data, which is of great limitation. Therefore, it is imperative to establish more accurate and data-compatible methods for predicting the severity of AP. The artificial intelligence (AI) algorithm based on artificial neural network (ANN) allow for the adaptive feature extraction for objective task through its internal complex network, instead of the hand-crafted methods commonly used in traditional machine learning (ML) algorithms. In this study, we delve into the final severity classification prediction of newly admitted AP patients, using deep learning (DL) algorithm to develop multi-view models, incorporated with patients' demographic information, vital signs, AP-related laboratory indexes and admission computed tomography (CT) images. The pancreatitis database in the platform of Clinical Data Research Center of Acute Abdominal Surgery at the First Affiliated Hospital of Dalian Medical University was used to gather AP cases. Deep neural network (DNN) and convolutional neural network (CNN) were utilized to construct models. The DNN prediction models with clinical data as input, the CNN prediction models with admission CT as input, and the multi-view models combining the two inputs were respectively established to predict the severity of AP. DL models for AP severity classification based on clinical indexes, imaging data and merged data were constructed. The multi-view model based on merged data offered more accurate prediction of the final severity classification of AP, with an overall accuracy rate of 80.26% (95% confidence interval (CI): 79.58%-80.94%). The constituent accuracy rates for mild acute pancreatitis, moderately severe acute pancreatitis, and severe acute pancreatitis were 91.69% (95% CI: 87.80%-95.57%), 64.90% (95% CI: 58.85%-70.95%), and 75.56% (95% CI: 68.58%-82.53%), respectively. The multi-view models using clinical indexes and imaging data as input outperform single-view models in AP severity prediction.