Comparison of Artificial Intelligence Techniques for Project Conceptual Cost Prediction: A Case Study and Comparative Analysis

Aim: The majority of machine learning (ML) models in healthcare are built on retrospective data, much of which is collected without explicit patient consent for use in artificial intelligence (AI) and ML applications. The primary aim of this study was to evaluate whether clinicians and scientific researchers themselves consent to provide their own data for the training of ML models. Materials and Methods: This survey was conducted through an anonymous online survey, utilizing platforms such as Telegram, LinkedIn, and Viber. The target audience comprised specific international groups, primarily Russian, German, and English-speaking, of clinicians and scientific researchers. These participants ranged in their levels of expertise and experience, from beginners to veterans. The survey centered on a singular, pivotal question: “Do You Consent to the Use of Your Biological and Private Data for Training Machine Learning and AI Models?” Respondents had the option to choose from three responses: “Yes” and “No”. Results: The survey was conducted in January 2024. A total of 119 unique and verified individuals participated in the survey. The results revealed that only 50% of respondents (63 persons) expressed consent to provide their own data for the training of ML and AI models. Conclusion: In the development of ML and AI models, particularly open-source ones, it is crucial to ascertain whether participants are willing to provide their private data. While ML algorithms can transform the nature of data, it is important to remember that the primary owner of this data is the individual. Our findings show that in 50% of the cases, even participants from scientific research and clinical backgrounds – individuals typically accountable for ensuring data quality in AI and ML model development – do not consent to the use of their data in AI and ML settings. This highlights the need for more stringent consent processes and ethical considerations in the utilization of personal data in AI and ML research.

Accurate estimation of reference evapotranspiration (ETo) plays a vital role in irrigation and water resource planning. The Penman–Monteith method recommended by the Food and Agriculture Organization (FAO PM56) is widely used and considered a standard to calculate ETo. However, FAO PM56 cannot be used with limited meteorological variables, so it is compulsory to choose an alternative model for ETo estimation, which requires fewer variables. This study built ten machine learning (ML) models based on multi-function, neural network, and tree-based structure against the FAO PM56 method. For this purpose, time series temperature data on a monthly scale are only used to train ML models. The developed ML models were applied to estimate ETo at different test stations and the obtained results were compared with the FAO PM56 method to verify and validate their performance in ETo estimation for the selected stations. In addition, multiple statistical indicators, including root-mean-square error (RMSE), coefficient of determination (R2), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), and correlation coefficient (r) were calculated to compare the performance of each ML model on ETo estimation. Among the applied ML models, the ETo tree boost (TB) ML model outperformed the other ML models in estimating ETo in diverse climatic conditions based on statistical indicators (R2, NSE, r, RMSE, and MAE). Moreover, the observed R2, NSE, and r were the highest for the TB ML model, while RMSE and MAE were found to be the lowest at the study sites compared to other applied ML models. Lastly, ETo point data yielded from the TB ML model was used in an interpolation process to create monthly and annual ETo maps. Based on the ETo maps, this study suggests mainly a focus on areas with high ETo values and proper irrigation scheduling of crops to ensure water sustainability.

Real-World Surveillance of FDA-Cleared Artificial Intelligence Models: Rationale and Logistics.

High-risk breast lesions (HRLs) are associated with future risk of breast cancer. Considering the pathological subtypes, malignancy upgrade rate differs according to each subtype and depends on various factors such as clinical and radiological features and biopsy method. Using artificial intelligence and machine learning models in breast imaging, evaluations can be made in terms of risk estimation in different research areas. This study aimed to develop a machine learning model to distinguish HRL cases requiring surgical excision from lesions with a low risk of accompanying malignancy. A total of 94 patients who were diagnosed with HRL by image-guided biopsy between January 2008 and March 2020 were included in the study. A structured database was created with clinical and radiological characteristics and histopathological results. A machine learning prediction model was created to make binary classifications of lesions as malignant or benign. Random forest, decision tree, K-nearest neighbors, logistic regression, support vector machine (SVM), and multilayer perceptron machine learning algorithms were used. Among these algorithms, SVM was the most successful. The estimations of malignancy for each case detected by artificial intelligence were combined and statistical analyses were performed. Considering all cases, the malignancy upgrade rate was 24.5%. A significant association was observed between malignancy upgrade rate and lesion size (P = 0.004), presence of mammography findings (P = 0.022), and breast imaging-reporting and data system category (P = 0.001). A statistically significant association was also found between the artificial intelligence prediction model and malignancy upgrade rate (P < 0.001). With the SVM model, an 84% accuracy and 0.786 area-underthe- curve score were obtained in classifying the data as benign or malignant. Our artificial intelligence model (SVM) can predict HRLs that can be followed up with a lower risk of accompanying malignancy. Unnecessary surgeries can be reduced, or second line vacuum excisions can be performed in HRLs, which are mostly benign, by evaluating on a case-by-case basis, in line with radiology-pathology compatibility and by using an artificial intelligence model.

Data-based groundwater quality estimation and uncertainty analysis for irrigation agriculture

Study question Can an AI image-based model be trained to predict whether an endometrium, prior to progesterone, is receptive for successful embryo implantation from ultrasound images? Summary answer An endometrial receptivity AI model, utilizing ultrasound images and clinical features, is predictive of successful implantation and outperforms endometrial thickness as a predictor of implantation. What is known already Despite its importance in successful embryo implantation and subsequent live birth, endometrial receptivity is difficult to measure. The standard of care at some clinics is to cancel an embryo transfer if EMT is measuring less than 7 mm on ultrasound assessment – however, achieving an EMT threshold does not guarantee success, even with euploid blastocysts. Currently available receptivity tests (e.g. endometrial receptivity assays) are unreliable and have not shown significant improvements in reproductive outcomes, necessitating the development of new evaluation methods for endometrial receptivity. AI can potentially address this gap in clinical care and understanding, aiming to increase patient success. Study design, size, duration 79,602 ultrasound images (40,910 patients) of the endometrium, on the day of progesterone start according to patient’s frozen embryo transfer cycle, were retrospectively collected from 4 clinic networks including 70 clinic locations. PGT-A outcomes and blastocyst quality were available for 15,466 and 36,070 blastocysts respectively, with 14,538 having labels for both. Different image samples (based on embryo quality) were selected to train and test several AI models, with implantation (positive beta-hCG) as a binary outcome. Participants/materials, setting, methods Three criteria for negative (implantation) class sample selection were tested for receptivity model development: 1) high- or medium-quality and/or euploid blastocysts; 2) high-quality blastocysts; 3) all blastocysts. Models were ensembles consisting of an image-based deep learning (DL)-model and a feature-based machine learning (ML)-model. Model performance was measured by Area-under-the-curve (AUC), sensitivity, and specificity. Feature importance from the ML model was assessed, while receptivity model performance was compared to the power of EMT to predict implantation. Main results and the role of chance A receptivity model trained on scenario 1) (n = 27,424) achieved AUC 0.613, sensitivity 0.716, and specificity 0.439 on a test set of 9197 samples, in predicting implantation. Another receptivity model trained on scenario 2) (n = 25,625) achieved AUC 0.627, sensitivity 0.637, and specificity 0.543 on a test set of 8642. Whereas a third receptivity model trained on scenario 3) (n = 31,238) was selected for further analysis as it attained the best performance with AUC 0.631, sensitivity 0.628, and specificity 0.556 on a test set of 10,422. The ensemble model also utilizes relevant clinical features, which were ranked by importance to model predictions: EMT; progesterone blood test value; age at transfer; previous total embryo transfers; days between ultrasound date and transfer date; age at retrieval; and oocyte origin. EMT was assessed for its ability to predict implantation at thresholds between 5–20 mm. The threshold that best separates positive and negative implantation was determined by evaluating AUC on a validation set of 5682. A threshold of 8.8 mm achieved an AUC 0.576, sensitivity 0.694, and specificity 0.455 in predicting implantation on a test set of 5686. However, the receptivity model AUC (0.631) was significantly higher (p &amp;lt; 0.001; DeLong test for differences in AUC), outperforming EMT predictability. Limitations, reasons for caution The endometrial receptivity model was built and tested on retrospective frozen embryo transfer data. Results should be corroborated in prospective and non-selection studies, as well as in multiple geographical locations (all clinics included reside in the United States). The dataset requires greater diversification, with a greater representation of EMT (&amp;lt;7mm). Wider implications of the findings This study indicates that an AI model can be built to predict implantation from ultrasound images and clinical features, surpassing current standards - EMT alone. Further experimentation, with additional clinical features, may improve performance to facilitate development of a model that can accurately predict the gold-standard of live birth. Trial registration number Not applicable

Artificial intelligence for patient scheduling in the real-world health care setting: A metanarrative review

Applications of artificial intelligence (AI) are pervasive in modern biomedical science. In fact, research results suggesting algorithms and AI models for different target diseases and conditions are continuously increasing. While this situation undoubtedly improves the outcome of AI models, health care providers are increasingly unsure which AI model to use due to multiple alternatives for a specific target and the "black box" nature of AI. Moreover, the fact that studies rarely use guidelines in developing and reporting AI models poses additional challenges in trusting and adapting models for practical implementation. This review protocol describes the planned steps and methods for a review of the synthesized evidence regarding the quality of available guidelines and frameworks to facilitate AI applications in medicine. We will commence a systematic literature search using medical subject headings terms for medicine, guidelines, and machine learning (ML). All available guidelines, standard frameworks, best practices, checklists, and recommendations will be included, irrespective of the study design. The search will be conducted on web-based repositories such as PubMed, Web of Science, and the EQUATOR (Enhancing the Quality and Transparency of Health Research) network. After removing duplicate results, a preliminary scan for titles will be done by 2 reviewers. After the first scan, the reviewers will rescan the selected literature for abstract review, and any incongruities about whether to include the article for full-text review or not will be resolved by the third and fourth reviewer based on the predefined criteria. A Google Scholar (Google LLC) search will also be performed to identify gray literature. The quality of identified guidelines will be evaluated using the Appraisal of Guidelines, Research, and Evaluation (AGREE II) tool. A descriptive summary and narrative synthesis will be carried out, and the details of critical appraisal and subgroup synthesis findings will be presented. The results will be reported using the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analyses) reporting guidelines. Data analysis is currently underway, and we anticipate finalizing the review by November 2023. Guidelines and recommended frameworks for developing, reporting, and implementing AI studies have been developed by different experts to facilitate the reliable assessment of validity and consistent interpretation of ML models for medical applications. We postulate that a guideline supports the assessment of an ML model only if the quality and reliability of the guideline are high. Assessing the quality and aspects of available guidelines, recommendations, checklists, and frameworks-as will be done in the proposed review-will provide comprehensive insights into current gaps and help to formulate future research directions. DERR1-10.2196/47105.

e16184 Background: Hepatocellular carcinoma (HCC) is a global health challenge, ranking sixth in incidence and third in cancer-related mortality. Microvascular invasion (MVI) is a crucial prognostic marker influencing recurrence rates and survival. Accurate preoperative MVI detection can guide surgical planning but is limited by invasive histopathological exams and interobserver variability. This study evaluates the diagnostic performance of artificial intelligence (AI) models, including machine learning (ML) and deep learning (DL), in predicting MVI in HCC using imaging modalities such as CT, MRI, ultrasound, PET/CT, and histopathology. Methods: A systematic review and meta-analysis followed PRISMA 2020 guidelines, covering studies from 2010 to 2023. Comprehensive searches were conducted across PubMed, Scopus, Web of Science, Embase, Cochrane Library, Google Scholar, European PMC, and BioMed Central. AI models were assessed for diagnostic accuracy using QUADAS-2 and Radiomics Quality Score (RQS). Metrics like the area under the curve (AUC), sensitivity, and specificity were analyzed. Results: This meta-analysis synthesized data from 51 studies, encompassing 6,257 records. DL models showed a pooled AUC of 0.84 (95% CI: 0.80–0.86), with sensitivity and specificity of 0.79 (95% CI: 0.75–0.82) and 0.84 (95% CI: 0.79–0.88), respectively. ML models achieved a pooled AUC of 0.83 (95% CI: 0.80–0.86), sensitivity of 0.79 (95% CI: 0.71–0.85), and higher specificity of 0.88 (95% CI: 0.84–0.92). Across imaging modalities, MRI and CT-based models achieved pooled AUCs of 0.87 and 0.83 for DL and 0.85 and 0.82 for ML, respectively. Ultrasound-based models demonstrated higher specificity but slightly lower sensitivity. Models incorporating clinical features did not outperform purely radiomics-based approaches. Quality assessments revealed low bias risks in patient selection (88%), index tests (94%), and reference standards (98%). However, only 51% of studies addressed inter-scanner variability, and 55% incorporated calibration or resampling. The mean RQS was 40%, with 84% adhering to robust imaging protocols. Conclusions: AI models, particularly DL, exhibit robust accuracy in predicting MVI in HCC, showing promise for integration into clinical workflows. These tools could enable personalized preoperative planning, improving patient outcomes and reducing recurrence risks. Standardized protocols, prospective validation, and broader adoption of advanced AI methods are needed to ensure consistent clinical utility and cost-effectiveness.

Artificial Intelligence: The Future of Maxillofacial Prognosis and Diagnosis?

This research study proposed a novel approach utilising AI models to predict the metallurgical performance of complex sulfide ore flotation. Five machine learning and artificial intelligence models were employed in this study, that is, Random Forest (RF), Artificial Neural Networks (ANN), Adaptive Neuro Fuzzy Interference System (ANFIS), Mamdani Fuzzy Logic (MFL) and Hybrid Neural Fuzzy Interference System (HyFIS). Sixty-two flotation tests were conducted on samples containing galena, chalcopyrite and sphalerite as the main valuable minerals, and pyrite as the main gangue mineral. Different variables were used as inputs in the AI studies including physiochemical and operational parameters. The flotation recovery of lead and copper and their corresponding grades in the bulk concentrate were the primary dependent variables (outputs). The input variables included the dosages of sodium cyanide (pyrite's depressant), sodium isopropyl xanthate (collector), zinc sulfate (sphalerite's depressant) and Methyl isobutyl carbinol (MIBC, frother); air flow rate; flotation time; and the speed of the impeller in the flotation cell, which is indicative of the energy input. For the purpose of AI model development, datasets were divided into two subsets. The first subset was primarily used for the training phase, and it comprised 80% of the total data. The second subset, consisting of 20% of the total data, was used for testing. The models’ performance was assessed using two main indicators: R-squared (R 2 ) for the proportion of explained variation and RMSE for the average prediction error. The Hybrid Neural Fuzzy Interference System demonstrated superior performance in predicting the recovery and grade of copper and lead, with R² and RMSE of 0.9895 and 1.069 for the training phase, respectively, whereas for the testing step the respective values were 0.9128 and 2.859.

Enhancing process monitoring and control in novel carbon capture and utilization biotechnology through artificial intelligence modeling: An advanced approach toward sustainable and carbon-neutral wastewater treatment.

The construction industry is rapidly adopting Industry 4.0 technologies, creating new opportunities to address persistent environmental and operational challenges. This review focuses on how Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are being leveraged to tackle these issues. It specifically explores AI’s role in predicting air pollution, improving material quality, monitoring worker health and safety, and enhancing Cyber-Physical Systems (CPS) for construction. This study evaluates various AI and ML models, including Artificial Neural Networks (ANNs) and Support Vector Machines SVMs, as well as optimization techniques like whale and moth flame optimization. These tools are assessed for their ability to predict air pollutant levels, improve concrete quality, and monitor worker safety in real time. Research papers were also reviewed to understand AI’s application in predicting the compressive strength of materials like cement mortar, fly ash, and stabilized clay soil. The performance of these models is measured using metrics such as coefficient of determination (R2), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Furthermore, AI has shown promise in predicting and reducing emissions of air pollutants such as PM2.5, PM10, NO2, CO, SO2, and O3. In addition, it improves construction material quality and ensures worker safety by monitoring health indicators like standing postures, electrocardiogram, and galvanic skin response. It is also concluded that AI technologies, including Explainable AI and Petri Nets, are also making advancements in CPS for the construction industry. The models’ performance metrics indicate they are well-suited for real-time construction operations. The study highlights the adaptability and effectiveness of these technologies in meeting current and future construction needs. However, gaps remain in certain areas of research, such as broader AI integration across diverse construction environments and the need for further validation of models in real-world applications. Finally, this research underscores the potential of AI and ML to revolutionize the construction industry by promoting sustainable practices, improving operational efficiency, and addressing safety concerns. It also provides a roadmap for future research, offering valuable insights for industry stakeholders interested in adopting AI technologies.

The research focus was motivated by the limited understanding of cognitive technologies and the growing gap between artificial intelligence (AI) and human intelligence. The research is a literature review, and its purpose is to simplify the meaning and processes behind cognitive technologies, notably, the fundamentals of machine learning (ML) and computer vision with the intention to briefly address the alleged threat of AI taking over the job market. The research is a review of peer-reviewed articles retrieved from comparative studies, systematic reviews, meta-analysis, service research, reports, conference proceedings, experimental studies, literature reviews, scientometric analyses, books, and multi-case studies, dating from the years of 2018 to 2024. This literature review defines machine learning (ML), artificial intelligence (AI), computer vision, and convolutional neural networks (CNNs). It also compares machine learning to traditional programming and reveals the types of learning in ML models’ training. ML and its correlation with AI are also discussed and details about theory of mind, self-aware AI, reactive machines, and limited memory AI are shared. The literature expounds computer vision, particularly convolutional neural network (CNN) and CNN layers. Recent cutting-edge applications of artificial intelligence including generative AI models and autonomous systems are also incorporated. Finally, the literature briefly addresses the alleged threat of AI taking over the job market. The findings of this literature review reveal that AI is becoming the new way of operating. The conclusion shows that AI models require significant computation to allow computers to learn autonomously. Thus, understanding mathematical models of data and perfecting the process of writing software could be the key to remaining employable as more jobs are expected to be shifted due to AI and tasks automation. Keywords: Cognitive technology, artificial intelligence, machine learning, computer vision, convolutional neural networks

Artificial intelligence and Machine Learning approaches in sports: Concepts, applications, challenges, and future perspectives