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General General

Evaluation of the Morphological and Biological Functions of Vascularized Microphysiological Systems with Supervised Machine Learning.

In Annals of biomedical engineering ; h5-index 52.0

Vascularized microphysiological systems and organoids are contemporary preclinical experimental platforms representing human tissue or organ function in health and disease. While vascularization is emerging as a necessary physiological organ-level feature required in most such systems, there is no standard tool or morphological metric to measure the performance or biological function of vascularized networks within these models. Further, the commonly reported morphological metrics may not correlate to the network's biological function-oxygen transport. Here, a large library of vascular network images was analyzed by the measure of each sample's morphology and oxygen transport potential. The oxygen transport quantification is computationally expensive and user-dependent, so machine learning techniques were examined to generate regression models relating morphology to function. Principal component and factor analyses were applied to reduce dimensionality of the multivariate dataset, followed by multiple linear regression and tree-based regression analyses. These examinations reveal that while several morphological data relate poorly to the biological function, some machine learning models possess a relatively improved, but still moderate predictive potential. Overall, random forest regression model correlates to the biological function of vascular networks with relatively higher accuracy than other regression models.

Tronolone James J, Mathur Tanmay, Chaftari Christopher P, Jain Abhishek

2023-Mar-13

Data science, Machine learning, Microphysiological systems, Vascularization

General General

Predicting drug shortages using pharmacy data and machine learning.

In Health care management science

Drug shortages are a global and complex issue having negative impacts on patients, pharmacists, and the broader health care system. Using sales data from 22 Canadian pharmacies and historical drug shortage data, we built machine learning models predicting shortages for the majority of the drugs in the most-dispensed interchangeable groups in Canada. When breaking drug shortages into four classes (none, low, medium, high), we were able to correctly predict the shortage class with 69% accuracy and a kappa value of 0.44, one month in advance, without access to any inventory data from drug manufacturers and suppliers. We also predicted 59% of the shortages deemed to be most impactful (given the demand for the drugs and the potential lack of interchangeable options). The models consider many variables, including the average days of a drug supply per patient, the total days of a drug supply, previous shortages, and the hierarchy of drugs within different drug groups and therapeutic classes. Once in production, the models will allow pharmacists to optimize their orders and inventories, and ultimately reduce the impact of drug shortages on their patients and operations.

Pall Raman, Gauthier Yvan, Auer Sofia, Mowaswes Walid

2023-Mar-13

Analytics, Drugs, Machine learning, Pharmacies, Shortages, Supply chain, Therapeutics

Radiology Radiology

A survey of ASER members on artificial intelligence in emergency radiology: trends, perceptions, and expectations.

In Emergency radiology

PURPOSE : There is a growing body of diagnostic performance studies for emergency radiology-related artificial intelligence/machine learning (AI/ML) tools; however, little is known about user preferences, concerns, experiences, expectations, and the degree of penetration of AI tools in emergency radiology. Our aim is to conduct a survey of the current trends, perceptions, and expectations regarding AI among American Society of Emergency Radiology (ASER) members.

METHODS : An anonymous and voluntary online survey questionnaire was e-mailed to all ASER members, followed by two reminder e-mails. A descriptive analysis of the data was conducted, and results summarized.

RESULTS : A total of 113 members responded (response rate 12%). The majority were attending radiologists (90%) with greater than 10 years' experience (80%) and from an academic practice (65%). Most (55%) reported use of commercial AI CAD tools in their practice. Workflow prioritization based on pathology detection, injury or disease severity grading and classification, quantitative visualization, and auto-population of structured reports were identified as high-value tasks. Respondents overwhelmingly indicated a need for explainable and verifiable tools (87%) and the need for transparency in the development process (80%). Most respondents did not feel that AI would reduce the need for emergency radiologists in the next two decades (72%) or diminish interest in fellowship programs (58%). Negative perceptions pertained to potential for automation bias (23%), over-diagnosis (16%), poor generalizability (15%), negative impact on training (11%), and impediments to workflow (10%).

CONCLUSION : ASER member respondents are in general optimistic about the impact of AI in the practice of emergency radiology and its impact on the popularity of emergency radiology as a subspecialty. The majority expect to see transparent and explainable AI models with the radiologist as the decision-maker.

Agrawal Anjali, Khatri Garvit D, Khurana Bharti, Sodickson Aaron D, Liang Yuanyuan, Dreizin David

2023-Mar-13

Artificial intelligence, Computer-aided detection, Emergency, Emergency radiology, Imaging, Machine learning, Radiology, Survey, Trauma

General General

Attitudes of Different Religions Toward Surrogacy: Analysis of 11 Countries' Situation Using Machine Learning Approach and Artificial Neural Networks.

In Journal of religion and health

Individuals may develop different attitudes on bioethics in general and reproductive ethics in particular, due to the effects of different sociocultural environments. Individuals' attitudes toward surrogacy are affected positively or negatively depending on religious and cultural environments. This study was conducted to determine and compare the attitudes of different religions toward surrogacy. This study is cross-sectional and collected from individuals living in Turkey, India, Iran, the Turkish Republic of Northern Cyprus, Madagascar, Nepal, Nigeria, Pakistan, Mexico, England, and Japan between May 2022 and December 2022. The study was conducted with individuals belonging to Islam, Christianity, Hinduism, Buddhism, and Atheism. The study was conducted with 1177 individuals from different religions who agreed to participate in the study by snowball sampling method. The introductory Information Form and "Attitude Questionnaire Toward Surrogacy" were used as data collection tools. R programming language 4.1.3 was used for regression analysis with machine learning approach and artificial neural networks, and SPSS-25 was used for other statistical analyses. There was a significant difference between the total mean score of the individuals' Attitudes toward Surrogacy Questionnaire and their religious beliefs (p < 0.05). When the results of the analysis of the regression model with the dummy variable, which was carried out with the aim of revealing the effects of religious belief on the attitude toward surrogacy, are examined, statistical estimates of the regression model show that the model is significant and usable F(4,1172) = 5.005, p = 0.001). It explains 1.7% of the total variance of the level of religious belief's attitude toward surrogacy. In the regression model, when the t-test results regarding the significance of the regression coefficient are examined, among the participants, it was determined that the mean score of those who believed in Islam (t =  - 3.827, p < 0.001) and those who believed in Christianity (t =  - 2.548, p < 0.001) was lower than the mean score of those who believed in Hinduism (Constant) (p < 0.05). Individuals' attitudes toward surrogacy differ according to their religion. The best performing algorithm for the prediction model was random forest (RF) regression. The contributions of the variables to the model were calculated with Shapley values (Shapley Additive Explanations (SHAP)). The SHAP values of the variables in the best performing model were examined to avoid bias in terms of comparison in the performance criterion. SHAP values (Shapley Additive Explanations) show the contribution or importance of each variable in the estimation of the model. It is determined that the most important variable that should be in the model to predict the Attitude Toward Surrogacy Survey variable is the Nationality variable. It is recommended that studies on attitudes toward surrogacy should be conducted by taking religious and cultural values into consideration.

Yıldız Metin, Felix Ezomo Ojeiru, Ademiju Olugbenga, Noibi Tajudeen Oluwafemi, Gomes Roseline Florence, Tanimowo Abraham, Tayyeb Muhammed, Khadka Ram Bahadur, Rhino Andrianirina, Yildiz Rabia, Ramazanzadegan Kiarash, Yildirim Mehmet Salih, Solmaz Ebru, Haylı Çiğdem Müge, Şengan Aylin

2023-Mar-13

Attitude, Religious belief, Surrogacy

Surgery Surgery

Artificial intelligence applications and ethical challenges in oral and maxillo-facial cosmetic surgery: a narrative review.

In Maxillofacial plastic and reconstructive surgery

Artificial intelligence (AI) refers to using technologies to simulate human cognition to solve a specific problem. The rapid development of AI in the health sector has been attributed to the improvement of computing speed, exponential increase in data production, and routine data collection. In this paper, we review the current applications of AI for oral and maxillofacial (OMF) cosmetic surgery to provide surgeons with the fundamental technical elements needed to understand its potential. AI plays an increasingly important role in OMF cosmetic surgery in various settings, and its usage may raise ethical issues. In addition to machine learning algorithms (a subtype of AI), convolutional neural networks (a subtype of deep learning) are widely used in OMF cosmetic surgeries. Depending on their complexity, these networks can extract and process the elementary characteristics of an image. They are, therefore, commonly used in the diagnostic process for medical images and facial photos. AI algorithms have been used to assist surgeons with diagnosis, therapeutic decisions, preoperative planning, and outcome prediction and evaluation. AI algorithms complement human skills while minimizing shortcomings through their capabilities to learn, classify, predict, and detect. This algorithm should, however, be rigorously evaluated clinically, and a systematic ethical reflection should be conducted regarding data protection, diversity, and transparency. It is possible to revolutionize the practice of functional and aesthetic surgeries with 3D simulation models and AI models. Planning, decision-making, and evaluation during and after surgery can be improved with simulation systems. A surgical AI model can also perform time-consuming or challenging tasks for surgeons.

Rokhshad Rata, Keyhan Seied Omid, Yousefi Parisa

2023-Mar-13

Artificial intelligence, Deep learning, Machine learning, Orthognathic surgery, Rhinoplasty

Radiology Radiology

Differentiating peritoneal tuberculosis and peritoneal carcinomatosis based on a machine learning model with CT: a multicentre study.

In Abdominal radiology (New York)

PURPOSE : It is still a challenge to make early differentiation of peritoneal tuberculosis (PTB) and peritoneal carcinomatosis (PC) clinically as well as on imaging and laboratory tests. We aimed to develop a model to differentiate PTB from PC based on clinical characteristics and primary CT signs.

METHODS : This retrospective study included 88 PTB patients and 90 PC patients (training cohort: 68 PTB patients and 69 PC patients from Beijing Chest Hospital; testing cohort: 20 PTB patients and 21 PC patients from Beijing Shijitan Hospital). The images were analyzed for omental thickening, peritoneal thickening and enhancement, small bowel mesentery thickening, the volume and density of ascites, and enlarged lymph nodes (LN). Meaningful clinical characteristics and primary CT signs comprised the model. ROC curve was used to validate the capability of the model in the training and testing cohorts.

RESULTS : There were significant differences in the following aspects between the two groups: (1) age; (2) fever; (3) night sweat; (4) cake-like thickening of the omentum and omental rim (OR) sign; (5) irregular thickening of the peritoneum, peritoneal nodules, and scalloping sign; (6) large ascites; and (7) calcified and ring enhancement of LN. The AUC and F1 score of the model were 0.971 and 0.923 in the training cohort and 0.914 and 0.867 in the testing cohort.

CONCLUSION : The model has the potential to distinguish PTB from PC and thus has the potential to be a diagnostic tool.

Pang Yu, Li Ye, Xu Dong, Sun Xiaoli, Hou Dailun

2023-Mar-13

Computed tomography, Machine learning, Peritoneal carcinomatosis, Peritoneal tuberculosis