ML Applications Research Articles

Performance and clinical applicability of machine learning in liver computed tomography imaging: a systematic review

ObjectivesMachine learning (ML) for medical imaging is emerging for several organs and image modalities. Our objectives were to provide clinicians with an overview of this field by answering the following questions: (1) How is ML applied in liver computed tomography (CT) imaging? (2) How well do ML systems perform in liver CT imaging? (3) What are the clinical applications of ML in liver CT imaging?MethodsA systematic review was carried out according to the guidelines from the PRISMA-P statement. The search string focused on studies containing content relating to artificial intelligence, liver, and computed tomography.ResultsOne hundred ninety-one studies were included in the study. ML was applied to CT liver imaging by image analysis without clinicians’ intervention in majority of studies while in newer studies the fusion of ML method with clinical intervention have been identified. Several were documented to perform very accurately on reliable but small data. Most models identified were deep learning-based, mainly using convolutional neural networks. Potentially many clinical applications of ML to CT liver imaging have been identified through our review including liver and its lesion segmentation and classification, segmentation of vascular structure inside the liver, fibrosis and cirrhosis staging, metastasis prediction, and evaluation of chemotherapy.ConclusionSeveral studies attempted to provide transparent result of the model. To make the model convenient for a clinical application, prospective clinical validation studies are in urgent call. Computer scientists and engineers should seek to cooperate with health professionals to ensure this.Key Points• ML shows great potential for CT liver image tasks such as pixel-wise segmentation and classification of liver and liver lesions, fibrosis staging, metastasis prediction, and retrieval of relevant liver lesions from similar cases of other patients.• Despite presenting the result is not standardized, many studies have attempted to provide transparent results to interpret the machine learning method performance in the literature.• Prospective studies are in urgent call for clinical validation of ML method, preferably carried out by cooperation between clinicians and computer scientists.

Awareness among teaching on AI and ML applications based on fuzzy in education sector at USA.

This paper summarises the level of knowledge held by educators in the United States on the use of artificial intelligence and machine learning in the classroom. The education industry seems to have reaped little benefits from the AI & ML industry's growth thus far. In any event, the creation of new ML & AI-based systems is mostly aimed towards areas with higher societal needs, such as medical diagnostics and individual transportation, rather than institutions of higher education. With this analysis, we want to shed some light on the mysterious state of application development in the US education industry. The report was written using a triangulation of research approaches to achieve this objective. First, we surveyed the current state-of-the-art reports from other countries and reviewed the relevant literature on AI & ML applications in the field of education. In the second phase, we analysed, to the extent possible, official documents from the United States education sector that dealt with AI and ML based on fuzzy digitalization initiatives. Third, in order to corroborate and expand upon the impressions received from the relevant literature and the document analysis, 15 guideline-based expert interviews were undertaken. Based on this data, we provide a selection of the AI & ML systems in use in universities and colleges now, analyse the benefits and drawbacks of implementing them, and speculate on their potential future evolution. While it would be a stretch to say that this paper presents a comprehensive overview of the subject, it does provide light on key areas of application and potential future research directions for AI and ML.

Evaluation of the dataset quality in gamma passing rate predictions using machine learning methods.

Gamma passing rate (GPR) predictions using machine learning methods have been explored for treatment verification of radiotherapy plans. However, these methods presented datasets with unbalanced number of plans having different treatment conditions (heterogeneous datasets), such as anatomical sites or dose per fractions, leading to lower model interpretability and prediction performance. We investigated the impact of the dataset composition on GPR binary classification (pass/fail) using random forest (RF), XG-boost, and neural network (NN) models. 945 plans were used to create one reference dataset (randomly assembled) and 24 customized datasets that considered four heterogeneity factors independently (anatomical region, number of arcs, dose per fraction, and treatment unit). 309 predictor features were extracted and calculated from plan parameters, modulation complexity metrics, and radiomic analysis (leave-trajectory maps, 3D dose distributions, and portal dosimetry images). The models' performances were measured using the area under the curve from the receiver operating characteristic (ROC-AUC). Radiomics features for reference models increased ROC-AUC values up to 13%, 15%, and 5% for RF, XG-Boost, and NN, respectively. The datasets with higher heterogeneous conditions presented the lower ROC-AUC values (RF: 0.72 ± 0.11, XG-Boost: 0.67 ± 0.1, NN: 0.89 ± 0.05) compared to models with less heterogeneous treatment conditions (RF: 0.88 ± 0.06, XG-Boost: 0.89 ± 0.07, NN: 0.98 ± 0.01). The ten most important features for each heterogeneity dataset group demonstrated their correlation with the treatments' physical aspects and GPR prediction. Improvements in data generalization and model performances can be associated with datasets having similar treatment conditions. This analysis might be implemented to evaluate the dataset quality and model consistency of further ML applications in radiotherapy. Dataset heterogeneities decrease ML model performance and reliability.

A framework to identify ethical concerns with ML-guided care workflows: a case study of mortality prediction to guide advance care planning.

Identifying ethical concerns with ML applications to healthcare (ML-HCA) before problems arise is now a stated goal of ML design oversight groups and regulatory agencies. Lack of accepted standard methodology for ethical analysis, however, presents challenges. In this case study, we evaluate use of a stakeholder "values-collision" approach to identify consequential ethical challenges associated with an ML-HCA for advanced care planning (ACP). Identification of ethical challenges could guide revision and improvement of the ML-HCA. We conducted semistructured interviews of the designers, clinician-users, affiliated administrators, and patients, and inductive qualitative analysis of transcribed interviews using modified grounded theory. Seventeen stakeholders were interviewed. Five "values-collisions"-where stakeholders disagreed about decisions with ethical implications-were identified: (1) end-of-life workflow and how model output is introduced; (2) which stakeholders receive predictions; (3) benefit-harm trade-offs; (4) whether the ML design team has a fiduciary relationship to patients and clinicians; and, (5) how and if to protect early deployment research from external pressures, like news scrutiny, before research is completed. From these findings, the ML design team prioritized: (1) alternative workflow implementation strategies; (2) clarification that prediction was only evaluated for ACP need, not other mortality-related ends; and (3) shielding research from scrutiny until endpoint driven studies were completed. In this case study, our ethical analysis of this ML-HCA for ACP was able to identify multiple sites of intrastakeholder disagreement that mark areas of ethical and value tension. These findings provided a useful initial ethical screening.

Application of Artificial Intelligence Techniques for Monkeypox: A Systematic Review.

Monkeypox or Mpox is an infectious virus predominantly found in Africa. It has spread to many countries since its latest outbreak. Symptoms such as headaches, chills, and fever are observed in humans. Lumps and rashes also appear on the skin (similar to smallpox, measles, and chickenpox). Many artificial intelligence (AI) models have been developed for accurate and early diagnosis. In this work, we systematically reviewed recent studies that used AI for mpox-related research. After a literature search, 34 studies fulfilling prespecified criteria were selected with the following subject categories: diagnostic testing of mpox, epidemiological modeling of mpox infection spread, drug and vaccine discovery, and media risk management. In the beginning, mpox detection using AI and various modalities was described. Other applications of ML and DL in mitigating mpox were categorized later. The various machine and deep learning algorithms used in the studies and their performance were discussed. We believe that a state-of-the-art review will be a valuable resource for researchers and data scientists in developing measures to counter the mpox virus and its spread.

A Survey: Network Feature Measurement Based on Machine Learning

In network management, network measuring is crucial. Accurate network measurements can increase network utilization, network management, and the ability to find network problems promptly. With extensive technological advancements, the difficulty for network measurement is not just the growth in users and traffic but also the increasingly difficult technical problems brought on by the network’s design becoming more complicated. In recent years, network feature measurement issues have been extensively solved by the use of ML approaches, which are ideally suited to thorough data analysis and the investigation of complicated network behavior. However, there is yet no favored learning model that can best address the network measurement issue. The problems that ML applications in the field of network measurement must overcome are discussed in this study, along with an analysis of the current characteristics of ML algorithms in network measurement. Finally, network measurement techniques that have been used as ML techniques are examined, and potential advancements in the field are explored and examined.

Harnessing Machine Learning in the Subsurface to Promote Operational Efficiency

Machine learning, despite having more than 50 years of history in subsurface disciplines, has largely remained a niche workflow, frequently performed in isolation with lack of repeatability. While advances in computing and programming language have opened up access to machine learning as a tool, we have yet to see the same growth in operational efficiency experienced by other segments and verticals within the energy industry. Application of ML toward data conditioning and workflow set-up could save geoscientists hundreds of hours each year, allowing for faster delivery of results and improving standardisation and consistency across departments.

Supervised machine learning for theory building and testing: Opportunities in operations management

AbstractMachine learning's (ML's) unique power to approximate functions and identify non‐obvious regularities in data have attracted considerable attention from researchers in natural and social sciences. The emergence of predictive modeling applications in OM studies notwithstanding, it remains unclear how OM scholars can effectively leverage supervised ML for theory building and theory testing, the primary goals of scientific research. We attempt to fill this gap by conducting a literature review of recent developments in supervised ML in OM to identify vacancies in the extant literature, shedding light on how ML applications can move beyond problem‐solving into theory building, and formulating a procedure to help OM scholars leverage ML for exploratory theory development. Our procedure employs the random forest with well‐developed properties and inference toolkits that are crucial for empirical research. We then expand the boundary of ML usage and connect supervised ML to the explanatory modeling and hypothesis testing employed by OM empiricists for decades, and discuss the use of supervised ML for causal inference from observational data. We posit that contemporary ML can facilitate pattern exploration and enhance the validity of theory testing. We conclude by discussing directions for future empirical OM studies that aim to leverage ML.

Management of Machine Learning Lifecycle Artifacts

The explorative and iterative nature of developing and operating ML applications leads to a variety of artifacts, such as datasets, features, models, hyperparameters, metrics, software, configurations, and logs. In order to enable comparability, reproducibility, and traceability of these artifacts across the ML lifecycle steps and iterations, systems and tools have been developed to support their collection, storage, and management. It is often not obvious what precise functional scope such systems offer so that the comparison and the estimation of synergy effects between candidates are quite challenging. In this paper, we aim to give an overview of systems and platforms which support the management of ML lifecycle artifacts. Based on a systematic literature review, we derive assessment criteria and apply them to a representative selection of more than 60 systems and platforms.

Process optimization using machine learning enhanced design of experiments (DOE): ranibizumab refolding as a case study

An application of ML enhanced DOE has been demonstrated to capture the process nonlinearity more effectively. The proposed approach is applied for modelling and optimization of protein refolding, resulting in 3% improved prediction efficiency.

THE USE OF SPEECH LEVELS BY MADURESE STUDENTS AS IMPLEMENTATION OF POLITENESS PRINCIPLES IN ISLAMIC BOARDING SCHOOL IN BONDOWOSO, EAST JAVA

This study examines the use of the Madurese speech levels at the Islamic Boarding School in Bondowoso. The study finds the form and factors behind the use of speech levels occuring at Islamic Boarding Schools considered to be the place to form the character of discipline and tawadlu'. The uniqueness of tawadhu’ culture (adab) in a Salaf Islamic boarding school affects the language and politeness manner. The study is qualitative research by the sociolinguistics study. The method of data collection is in the form of an observation of participation. The observations included the listening-engagement technique, the free listening-engagement technique, supported by recording and note-taking techniques and supplemented by the interview method. The data were collected and then written in the form of orthographic writing. The analytical method of this research was contextual analysis. The results showed: 1) the èngghi-bhunten only used in the dhelem family (Kiai and Nyai); 2) santri always use ML èngghi-bhunten to Kiai and Nyai as a reflection of the Salaf Islamic boarding schools and as a form of polite manners; 3) The use of ML èngghi-bhunten is a culture of the santri's tawadhu towards Kyai and Nyai. The novelty of the results of this study lies in the use of ML Engghi-bhunten as a form of applying the student’s culture of tawadhu to Kyai and Nyai. The application of Engghi-bhunten ML as a venue for instilling morals and building student character in being polite and useful for avoiding conflicts such as offenses and misunderstandings in communication. Keywords: Islamic Boarding School, Politeness Principles, Èngghi-Bhunten Speech Level DOI： https://doi.org/10.35741/issn.0258-2724.58.2.9

A novel application of deep learning to forensic hair analysis methodology

ABSTRACT A deep learning model called HairNet was developed to conduct forensic hair analysis, including the classification of hair as human and suitability for nuclear DNA (nDNA) analysis. The training and testing data used were microscopic images of hair features including the medulla and the hair root. The final model iterations obtained 100% accuracy on the medulla dataset to classify hair as human or non-human and between 96% and 100% accuracy on the hair root dataset to classify human hair as suitable for nDNA analysis depending on the grouping of root types. The greatest impact on accuracy was the quantity and quality of the training and testing data and therefore the critical step in model development. The application of ML to forensic methodology is a novel and innovative approach and a means to improve objectivity; however, the creation of training and testing data initially requires expert human judgement and therefore collaboration is essential in the development of benchmark datasets. This research demonstrates how deep learning can be successfully applied to forensic methodology and the possibilities for other forensic disciplines.

Impact of measurement error on deep neural networks for nuclear material accountancy

Continued growth in the nuclear industry is resulting in an increased burden for regulators due to the costs associated with implementing traditional safeguards. One such stakeholder, the International Atomic Energy Agency (IAEA), has published a research and development roadmap of suggested improvements that would help safeguards remain effective and efficient in the face of growing demands. These priorities include identifying, evaluating, and testing promising applications of robotics and machine learning/artificial intelligence (ML/AI) to improve safeguards. Nuclear material accountancy (NMA) is of particular interest because traditional approaches require time and resource intensive destructive assay (DA) measurements. One desirable improvement would be utilization of unattended measurements in order to reduce the burden of frequent DA measurements required at large throughput facilities, even though such unattended measurements would likely have higher uncertainty. The application of ML could improve detection of small changes, potentially due to material theft, in these higher uncertainty measurements collected at nuclear facilities to enable near real time anomaly detection. However, unique challenges arise when attempting to use deep neural networks with datasets containing errors that are characterized by models commonly associated with safeguards measurements. The purpose of this paper is to outline both traditional and ML based approaches to NMA and compare them through example. It can be shown that traditional methods for NMA often offers superior performance to the proposed ML pipeline, which consists supervised regression and unsupervised classification, when trained on datasets that contain measurement errors. ML approaches may still be competitive with traditional methods for NMA, however, special care must be taken to mitigate the impact of measurement error that disproportionately affects deep learning approaches.

Про деякі аспекти інженерії програмних систем з елементами штучного інтелекту

AI-based software systems are rapidly spreading in various business areas. In this context, the unavoidable convergence of the Software Engineering and Artificial Intelligence and Machine Learning (AI/ML) disciplines is considered an obvious and one of the following significant challenges within the engineering process. The life cycle, models, and technologies of AI/ML elements are pretty specific, and this should be considered in software engineering to ensure their performance and compliance with business needs. AI/ML applications have some distinct characteristics compared to traditional software applications. Thus, several challenges and risk factors regarding AI/ML applications appear to software developers. To study the common challenges in AI/ML application development, we used two different perspectives: software engineering and machine learning. AI/ML applications, like other software systems, need a well-defined software engineering process for their development and maintenance. We discussed challenges and recommendations for different phases of the software development life cycle for ML applications, particularly requirement engineering, design, implementation, integration, testing, and deployment. AI/ML application development has specific aspects to consider as a software development project. We discussed the characteristics and recommendations concerning problem formulation, data acquisition, preprocessing, feature extraction, model building, evaluation, model integration and deployment, model management, and ethics in AI/ML development. In the work, there were formulated recommendations for each analyzed challenge that should be useful for software developers. The next stage of this research is the compilation of detailed systematic guidelines for the software development process for AI/ML systems.

Strategic Modeling of Medical Intelligence as a Countermeasure for Future Pandemics

The coronavirus disease 2019 (COVID-19) evidently caused untold suffering and disruption of activities worldwide. Several hundreds of millions of individuals were infected, resulting in millions of deaths and socioeconomic losses in all regions of the world. It is possible that availability of information required to make a quick decision would have prevented the consequences of the disease, suggesting that the COVID-19 pandemic was an indication of a global medical intelligence failure. Indeed, medical intelligence is a critical aspect of intelligence gathering, responsible for collection, analysis, interpretation and characterization of the data to effectively monitor the outbreak of diseases and activate the required modalities and strategic planning to provide prompt preparedness and response countermeasures (including nonmedical and medical such as rehabilitation) to manage the public health risk or mitigate the incident. In this context, the role of technology, especially AI, ML and DL applications to support the medical intelligence is explored into this paper. The study followed systematic literature review to gather the information and arrive at strategic model. The study extends practical implications by suggesting the role of AI, ML and DL in medical intelligence, that should be considered as a fundamental arm of the national security architecture of a nation. With tremendous progress in technologies, further enhancement of medical intelligence can help to mitigate potential public health risks and future pandemics. There is an urgent demand for a better knowledge of what tasks information structures and technology scientists can play in this global pandemic related to medical intelligence. Current paper thus details the critical components of medical intelligence systems and networks required as countermeasures for future pandemics.

Towards QoS-Based Embedded Machine Learning

Due to various breakthroughs and advancements in machine learning and computer architectures, machine learning models are beginning to proliferate through embedded platforms. Some of these machine learning models cover a range of applications including computer vision, speech recognition, healthcare efficiency, industrial IoT, robotics and many more. However, there is a critical limitation in implementing ML algorithms efficiently on embedded platforms: the computational and memory expense of many machine learning models can make them unsuitable in resource-constrained environments. Therefore, to efficiently implement these memory-intensive and computationally expensive algorithms in an embedded computing environment, innovative resource management techniques are required at the hardware, software and system levels. To this end, we present a novel quality-of-service based resource allocation scheme that uses feedback control to adjust compute resources dynamically to cope with the varying and unpredictable workloads of ML applications while still maintaining an acceptable level of service to the user. To evaluate the feasibility of our approach we implemented a feedback control scheduling simulator that was used to analyze our framework under various simulated workloads. We also implemented our framework as a Linux kernel module running on a virtual machine as well as a Raspberry Pi 4 single board computer. Results illustrate that our approach was able to maintain a sufficient level of service without overloading the processor as well as providing an energy savings of almost 20% as compared to the native resource management in Linux.

Technology Focus: Data Analytics (October 2022)

Oil and gas companies are starting to invest more in the energy transition and establish emissions-reduction goals, such as an end to routine gas venting and flaring, with some going as far as setting net-zero goals by mid-century. A wide range of climate technologies with varying degrees of maturity levels are needed to pave the road to net zero. These include electrification and grid decarbonization, advances in battery technology, blue and green hydrogen fuels, bioenergy, carbon capture use and storage, and mitigating emissions of potent greenhouse gases such as methane. The COVID-19 pandemic has helped accelerate the pace of the digital transformation in the oil and gas industry. Digitalization is, in effect, a part of the broader energy transition that is occurring in the industry. This includes a move from on-premises data centers to the cloud, building digital twins of physical assets, process automation using the Internet of Things (IoT), leveraging reams of data from oil and gas operations for artificial intelligence/machine leaning (AI/ML) applications, cloud-based high-performance computing for applications such as seismic imaging for carbon capture and storage, and predictive maintenance to fix leaky equipment to reduce the environmental footprint of operations. These technologies serve to improve productivity, increase operational efficiency, reduce downtime, increase cost savings, and reduce the carbon intensity of operations. The International Energy Agency report “The Oil and Gas Industry in Energy Transitions” states that reducing methane leaks to the atmosphere is the single most important and cost-effective way for the industry to bring down these emissions. This is a domain that can benefit significantly from the use of AI/ML techniques for leak detection and remediation (LDAR) efforts. ML techniques such as computer vision and anomaly detection can be used to identify both large and localized methane leaks from remote-sensing data and data streaming in from IoT sensors in the field. A combination of data modalities, with varying spatiotemporal resolutions, together with appropriate AI/ML technologies, would be essential for an effective methane LDAR program. As companies look to reduce their Scope 1, 2, and 3 emissions, breaking down data silos and establishing data standardization with common data models becomes critical in systems integration to develop an optimal emissions-reduction strategy. A seamless flow of information will enable the generation of high-fidelity data sets, which can be used to lower the operational footprint and drive business effects with AI solutions. With cloud-enabled technologies, driven by the application of ML and deep learning, companies can combine speed of implementation with scalability to accelerate their energy-transition efforts. I would like to invite readers to review the selection of papers to get an idea of various applications in the upstream oil and gas space where ML methods have been used. The highlighted papers cover the use of transformer-based models to predict oil production, the use of data analytics to study parent/child well relationships in shale plays, and the use of convolutional neural networks in core analysis. Recommended additional reading at OnePetro: www.onepetro.org. SPE 207744 Accelerating Subsurface Data Processing and Interpretation With Cloud-Based Full Waveform Inversion Systems by Sirivan Chaleunxay, Amazon Web Services, et al. SPE 205443 Natural-Language Processing and Text-Mining Approaches in Production-Shortfalls Analytics: Methodology, Case Study, and Value in the North Sea by Edgar Bernier, Total Denmark, et al. URTEC 208367 Real-Time Applications for Geological Operations: Repeatable AI Use Cases by Alfio Malossi, Eni, et al.

Systematic Analysis of Ovarian Cancer Empowered with Machine and Deep Learning: A Taxonomy and Future Challenges

Machine and Deep learning has witnessed an exceptional amount of admiration in recent years. ML has ability to learn data itself by predicting uncertain conditions or future and classify categories with minimum intervention of human. While in DL, computers are able to automatically, learn useful features and representation precisely from raw data. ML and DL potentially a disruptive technology in predictive healthcare analysis. A detailed understanding and evaluation of the applications and principles of radiomics, machine and deep learning is an important task to construct possible solutions that are capable of accomplish compulsory and ethical requirements, which can enhance efficiency, quality and outcomes. Machine and deep learning extensively being use in medical image analysis, medical diagnostics and medical image technology. The wide scope and sudden progress of ML and DL has remarkably change the ways of diagnosis, prediction, classification and analyzing ovarian, lungs, brain, skin and various other types of cancer. In view of multiple applications of ML and DL, in this article a review conducted on ovarian cancer. This review comprises the detailed analysis of OC (prognosis, diagnosis, classification, evaluation) by covering all the major contributions of machine and deep learning. Furthermore, a literature taxonomy of the research conferred and emerging aspects analyzed. A section of discussion also signified to elaborate the limitations and future challenges of machine and deep learning comprises with ovarian cancer.

Increasing trust and fairness in machine learning applications within the mortgage industry

The integration of machine learning in applications provides opportunities for increased efficiency in many organisations. However, the deployment of such systems is often hampered by the lack of insight into how their decisions are reached, resulting in concerns about trust and fairness. In this article, we investigate to what extent the addition of explainable AI components to ML applications can contribute to alleviating these issues. As part of this research, explainable AI functionality was developed for an existing ML model used for mortgage fraud detection at a large international financial institution based in The Netherlands A system implementing local explanation techniques was deployed to support the day-to-day work of fraud detection experts working with the model. In addition, a second system implementing global explanation techniques was developed to support the model management processes involving data-scientists, legal experts and compliance officers. A controlled experiment using actual mortgage applications was carried out to measure the effectiveness of these two systems, using both quantitative and qualitative assessment methods. Our results show that the addition of explainable AI functionality results in a statistically significant improvement in the levels of trust and usability by its daily users. The explainable AI system implementing global interpretability was found to considerably increase confidence in the ability to perform the processes focused on compliance and fairness. In particular, bias detection towards demographic groups successfully aided in the identification and removal of bias towards applicants with a migration background.

A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects.

BackgroundMachine learning (ML) and deep learning (DL) methods have recently garnered a great deal of attention in the field of cancer research by making a noticeable contribution to the growth of predictive medicine and modern oncological practices. Considerable focus has been particularly directed toward hematologic malignancies because of the complexity in detecting early symptoms. Many patients with blood cancer do not get properly diagnosed until their cancer has reached an advanced stage with limited treatment prospects. Hence, the state-of-the-art revolves around the latest artificial intelligence (AI) applications in hematology management.ObjectiveThis comprehensive review provides an in-depth analysis of the current AI practices in the field of hematology. Our objective is to explore the ML and DL applications in blood cancer research, with a special focus on the type of hematologic malignancies and the patient’s cancer stage to determine future research directions in blood cancer.MethodsWe searched a set of recognized databases (Scopus, Springer, and Web of Science) using a selected number of keywords. We included studies written in English and published between 2015 and 2021. For each study, we identified the ML and DL techniques used and highlighted the performance of each model.ResultsUsing the aforementioned inclusion criteria, the search resulted in 567 papers, of which 144 were selected for review.ConclusionsThe current literature suggests that the application of AI in the field of hematology has generated impressive results in the screening, diagnosis, and treatment stages. Nevertheless, optimizing the patient’s pathway to treatment requires a prior prediction of the malignancy based on the patient’s symptoms or blood records, which is an area that has still not been properly investigated.