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Fulltext The communication of artificial intelligence and deep learning in computer tomography image recognition of epidemic pulmonary infectious diseases

Wang W, Zhao X, Jia Y, Xu J

PLoS One, 2024;19(2):e0297578.
PMID: 38319912 DOI: 10.1371/journal.pone.0297578

The objectives are to improve the diagnostic efficiency and accuracy of epidemic pulmonary infectious diseases and to study the application of artificial intelligence (AI) in pulmonary infectious disease diagnosis and public health management. The computer tomography (CT) images of 200 patients with pulmonary infectious disease are collected and input into the AI-assisted diagnosis software based on the deep learning (DL) model, "UAI, pulmonary infectious disease intelligent auxiliary analysis system", for lesion detection. By analyzing the principles of convolutional neural networks (CNN) in deep learning (DL), the study selects the AlexNet model for the recognition and classification of pulmonary infection CT images. The software automatically detects the pneumonia lesions, marks them in batches, and calculates the lesion volume. The result shows that the CT manifestations of the patients are mainly involved in multiple lobes and density, the most common shadow is the ground-glass opacity. The detection rate of the manual method is 95.30%, the misdetection rate is 0.20% and missed diagnosis rate is 4.50%; the detection rate of the DL-based AI-assisted lesion method is 99.76%, the misdetection rate is 0.08%, and the missed diagnosis rate is 0.08%. Therefore, the proposed model can effectively identify pulmonary infectious disease lesions and provide relevant data information to objectively diagnose pulmonary infectious disease and manage public health.

Matched MeSH terms: Artificial Intelligence
Brain white and gray matter anatomy of MRI segmentation based on tissue evaluation

Salih QA, Ramli AR, Mahmud R, Wirza R

MedGenMed, 2005;7(2):1.
PMID: 16369380

Different approaches to gray and white matter measurements in magnetic resonance imaging (MRI) have been studied. For clinical use, the estimated values must be reliable and accurate when, unfortunately, many techniques fail on these criteria in an unrestricted clinical environment. A recent method for tissue clusterization in MRI analysis has the advantage of great simplicity, and it takes the account of partial volume effects. In this study, we will evaluate the intensity of MR sequences known as T1-weighted images in an axial sliced section. Intensity group clustering algorithms are proposed to achieve further diagnosis for brain MRI, which has been hardly studied. Subjective study has been suggested to evaluate the clustering group intensity in order to obtain the best diagnosis as well as better detection for the suspected cases. This technique makes use of image tissue biases of intensity value pixels to provide 2 regions of interest as techniques. Moreover, the original mathematic solution could still be used with a specific set of modern sequences. There are many advantages to generalize the solution, which give far more scope for application and greater accuracy.

Matched MeSH terms: Artificial Intelligence
An immune-inspired swarm aggregation algorithm for self-healing swarm robotic systems

Timmis J, Ismail AR, Bjerknes JD, Winfield AF

Biosystems, 2016 Aug;146:60-76.
PMID: 27178784 DOI: 10.1016/j.biosystems.2016.04.001

Swarm robotics is concerned with the decentralised coordination of multiple robots having only limited communication and interaction abilities. Although fault tolerance and robustness to individual robot failures have often been used to justify the use of swarm robotic systems, recent studies have shown that swarm robotic systems are susceptible to certain types of failure. In this paper we propose an approach to self-healing swarm robotic systems and take inspiration from the process of granuloma formation, a process of containment and repair found in the immune system. We use a case study of a swarm performing team work where previous works have demonstrated that partially failed robots have the most detrimental effect on overall swarm behaviour. We have developed an immune inspired approach that permits the recovery from certain failure modes during operation of the swarm, overcoming issues that effect swarm behaviour associated with partially failed robots.

Matched MeSH terms: Artificial Intelligence*
Fulltext Faculty perceptions regarding an individually tailored, flexible length, outcomes-based curriculum for undergraduate medical students

Shankar PR, Azhar T, Nadarajah VD, Er HM, Arooj M, Wilson IG

Korean J Med Educ, 2023 Sep;35(3):235-247.
PMID: 37670520 DOI: 10.3946/kjme.2023.262

PURPOSE: The perception of faculty members about an individually tailored, flexible-length, outcomes-based curriculum for undergraduate medical students was studied. Their opinion about the advantages, disadvantages, and challenges was also noted. This study was done to help educational institutions identify academic and social support and resources required to ensure that graduate competencies are not compromised by a flexible education pathway.
METHODS: The study was done at the International Medical University, Malaysia, and the University of Lahore, Pakistan. Semi-structured interviews were conducted from 1st August 2021 to 17th March 2022. Demographic information was noted. Themes were identified, and a summary of the information under each theme was created.
RESULTS: A total of 24 (14 from Malaysia and 10 from Pakistan) faculty participated. Most agreed that undergraduate medical students can progress (at a differential rate) if they attain the required competencies. Among the major advantages mentioned were that students may graduate faster, learn at a pace comfortable to them, and develop an individualized learning pathway. Several logistical challenges must be overcome. Providing assessments on demand will be difficult. Significant regulatory hurdles were anticipated. Artificial intelligence (AI) can play an important role in creating an individualized learning pathway and supporting time-independent progression. The course may be (slightly) cheaper than a traditional one.
CONCLUSION: This study provides a foundation to further develop and strengthen flexible-length competency-based medical education modules. Further studies are required among educators at other medical schools and in other countries. Online learning and AI will play an important role.

Matched MeSH terms: Artificial Intelligence
Technology, knowledge systems, population dynamics, and coastal ecosystems

Williams MJ

Ambio, 2002 Jun;31(4):337-9.
PMID: 12174604
Matched MeSH terms: Artificial Intelligence*
Fulltext Machine learning in internet financial risk management: A systematic literature review

Tian X, Tian Z, Khatib SFA, Wang Y

PLoS One, 2024;19(4):e0300195.
PMID: 38625972 DOI: 10.1371/journal.pone.0300195

Internet finance has permeated into myriad households, bringing about lifestyle convenience alongside potential risks. Presently, internet finance enterprises are progressively adopting machine learning and other artificial intelligence methods for risk alertness. What is the current status of the application of various machine learning models and algorithms across different institutions? Is there an optimal machine learning algorithm suited for the majority of internet finance platforms and application scenarios? Scholars have embarked on a series of studies addressing these questions; however, the focus predominantly lies in comparing different algorithms within specific platforms and contexts, lacking a comprehensive discourse and summary on the utilization of machine learning in this domain. Thus, based on the data from Web of Science and Scopus databases, this paper conducts a systematic literature review on all aspects of machine learning in internet finance risk in recent years, based on publications trends, geographical distribution, literature focus, machine learning models and algorithms, and evaluations. The research reveals that machine learning, as a nascent technology, whether through basic algorithms or intricate algorithmic combinations, has made significant strides compared to traditional credit scoring methods in predicting accuracy, time efficiency, and robustness in internet finance risk management. Nonetheless, there exist noticeable disparities among different algorithms, and factors such as model structure, sample data, and parameter settings also influence prediction accuracy, although generally, updated algorithms tend to achieve higher accuracy. Consequently, there is no one-size-fits-all approach applicable to all platforms; each platform should enhance its machine learning models and algorithms based on its unique characteristics, data, and the development of AI technology, starting from key evaluation indicators to mitigate internet finance risks.

Matched MeSH terms: Artificial Intelligence*
Fulltext Implementation of Nutrigenetics and Nutrigenomics Research and Training Activities for Developing Precision Nutrition Strategies in Malaysia

Dhanapal ACTA, Wuni R, Ventura EF, Chiet TK, Cheah ESG, Loganathan A, et al.

Nutrients, 2022 Dec 01;14(23).
PMID: 36501140 DOI: 10.3390/nu14235108

Nutritional epidemiological studies show a triple burden of malnutrition with disparate prevalence across the coexisting ethnicities in Malaysia. To tackle malnutrition and related conditions in Malaysia, research in the new and evolving field of nutrigenetics and nutrigenomics is essential. As part of the Gene-Nutrient Interactions (GeNuIne) Collaboration, the Nutrigenetics and Nutrigenomics Research and Training Unit (N2RTU) aims to solve the malnutrition paradox. This review discusses and presents a conceptual framework that shows the pathway to implementing and strengthening precision nutrition strategies in Malaysia. The framework is divided into: (1) Research and (2) Training and Resource Development. The first arm collects data from genetics, genomics, transcriptomics, metabolomics, gut microbiome, and phenotypic and lifestyle factors to conduct nutrigenetic, nutrigenomic, and nutri-epigenetic studies. The second arm is focused on training and resource development to improve the capacity of the stakeholders (academia, healthcare professionals, policymakers, and the food industry) to utilise the findings generated by research in their respective fields. Finally, the N2RTU framework foresees its applications in artificial intelligence and the implementation of precision nutrition through the action of stakeholders.

Matched MeSH terms: Artificial Intelligence*
Computer Aided Solution for Automatic Segmenting and Measurements of Blood Leucocytes Using Static Microscope Images

Abdulhay E, Mohammed MA, Ibrahim DA, Arunkumar N, Venkatraman V

J Med Syst, 2018 Feb 17;42(4):58.
PMID: 29455440 DOI: 10.1007/s10916-018-0912-y

Blood leucocytes segmentation in medical images is viewed as difficult process due to the variability of blood cells concerning their shape and size and the difficulty towards determining location of Blood Leucocytes. Physical analysis of blood tests to recognize leukocytes is tedious, time-consuming and liable to error because of the various morphological components of the cells. Segmentation of medical imagery has been considered as a difficult task because of complexity of images, and also due to the non-availability of leucocytes models which entirely captures the probable shapes in each structures and also incorporate cell overlapping, the expansive variety of the blood cells concerning their shape and size, various elements influencing the outer appearance of the blood leucocytes, and low Static Microscope Image disparity from extra issues outcoming about because of noise. We suggest a strategy towards segmentation of blood leucocytes using static microscope images which is a resultant of three prevailing systems of computer vision fiction: enhancing the image, Support vector machine for segmenting the image, and filtering out non ROI (region of interest) on the basis of Local binary patterns and texture features. Every one of these strategies are modified for blood leucocytes division issue, in this manner the subsequent techniques are very vigorous when compared with its individual segments. Eventually, we assess framework based by compare the outcome and manual division. The findings outcome from this study have shown a new approach that automatically segments the blood leucocytes and identify it from a static microscope images. Initially, the method uses a trainable segmentation procedure and trained support vector machine classifier to accurately identify the position of the ROI. After that, filtering out non ROI have proposed based on histogram analysis to avoid the non ROI and chose the right object. Finally, identify the blood leucocytes type using the texture feature. The performance of the foreseen approach has been tried in appearing differently in relation to the system against manual examination by a gynaecologist utilizing diverse scales. A total of 100 microscope images were used for the comparison, and the results showed that the proposed solution is a viable alternative to the manual segmentation method for accurately determining the ROI. We have evaluated the blood leucocytes identification using the ROI texture (LBP Feature). The identification accuracy in the technique used is about 95.3%., with 100 sensitivity and 91.66% specificity.

Matched MeSH terms: Artificial Intelligence
Fulltext Host-dependent molecular factors mediating SARS-CoV-2 infection to gain clinical insights for developing effective targeted therapy

Shafi G, Desai S, Srinivasan K, Ramesh A, Chaturvedi R, Uttarwar M

Mol Genet Genomics, 2021 May;296(3):501-511.
PMID: 33743061 DOI: 10.1007/s00438-021-01774-1

Coronavirus disease 2019 (COVID-19), a recent viral pandemic that first began in December 2019, in Hunan wildlife market, Wuhan, China. The infection is caused by a coronavirus, SARS-CoV-2 and clinically characterized by common symptoms including fever, dry cough, loss of taste/smell, myalgia and pneumonia in severe cases. With overwhelming spikes in infection and death, its pathogenesis yet remains elusive. Since the infection spread rapidly, its healthcare demands are overwhelming with uncontrollable emergencies. Although laboratory testing and analysis are developing at an enormous pace, the high momentum of severe cases demand more rapid strategies for initial screening and patient stratification. Several molecular biomarkers like C-reactive protein, interleukin-6 (IL6), eosinophils and cytokines, and artificial intelligence (AI) based screening approaches have been developed by various studies to assist this vast medical demand. This review is an attempt to collate the outcomes of such studies, thus highlighting the utility of AI in rapid screening of molecular markers along with chest X-rays and other COVID-19 symptoms to enable faster diagnosis and patient stratification. By doing so, we also found that molecular markers such as C-reactive protein, IL-6 eosinophils, etc. showed significant differences between severe and non-severe cases of COVID-19 patients. CT findings in the lungs also showed different patterns like lung consolidation significantly higher in patients with poor recovery and lung lesions and fibrosis being higher in patients with good recovery. Thus, from these evidences we perceive that an initial rapid screening using integrated AI approach could be a way forward in efficient patient stratification.

Matched MeSH terms: Artificial Intelligence*
Fulltext Diagnosis of Schizophrenia Based on the Data of Various Modalities: Biomarkers and Machine Learning Techniques (Review)

Sharaev MG, Malashenkova IK, Maslennikova AV, Zakharova NV, Bernstein AV, Burnaev EV, et al.

Sovrem Tekhnologii Med, 2022;14(5):53-75.
PMID: 37181835 DOI: 10.17691/stm2022.14.5.06

Schizophrenia is a socially significant mental disorder resulting frequently in severe forms of disability. Diagnosis, choice of treatment tactics, and rehabilitation in clinical psychiatry are mainly based on the assessment of behavioral patterns, socio-demographic data, and other investigations such as clinical observations and neuropsychological testing including examination of patients by the psychiatrist, self-reports, and questionnaires. In many respects, these data are subjective and therefore a large number of works have appeared in recent years devoted to the search for objective characteristics (indices, biomarkers) of the processes going on in the human body and reflected in the behavioral and psychoneurological patterns of patients. Such biomarkers are based on the results of instrumental and laboratory studies (neuroimaging, electro-physiological, biochemical, immunological, genetic, and others) and are successfully being used in neurosciences for understanding the mechanisms of the emergence and development of nervous system pathologies. Presently, with the advent of new effective neuroimaging, laboratory, and other methods of investigation and also with the development of modern methods of data analysis, machine learning, and artificial intelligence, a great number of scientific and clinical studies is being conducted devoted to the search for the markers which have diagnostic and prognostic value and may be used in clinical practice to objectivize the processes of establishing and clarifying the diagnosis, choosing and optimizing treatment and rehabilitation tactics, predicting the course and outcome of the disease. This review presents the analysis of the works which describe the correlates between the diagnosis of schizophrenia, established by health professionals, various manifestations of the psychiatric disorder (its subtype, variant of the course, severity degree, observed symptoms, etc.), and objectively measured characteristics/quantitative indicators (anatomical, functional, immunological, genetic, and others) obtained during instrumental and laboratory examinations of patients. A considerable part of these works has been devoted to correlates/biomarkers of schizophrenia based on the data of structural and functional (at rest and under cognitive load) MRI, EEG, tractography, and immunological data. The found correlates/biomarkers reflect anatomic disorders in the specific brain regions, impairment of functional activity of brain regions and their interconnections, specific microstructure of the brain white matter and the levels of connectivity between the tracts of various structures, alterations of electrical activity in various parts of the brain in different EEG spectral ranges, as well as changes in the innate and adaptive links of immunity. Current methods of data analysis and machine learning to search for schizophrenia biomarkers using the data of diverse modalities and their application during building and interpretation of predictive diagnostic models of schizophrenia have been considered in the present review.

Matched MeSH terms: Artificial Intelligence
Precision endoscopy in the era of climate change and sustainability

Ueda T, Li JW, Ho SH, Singh R, Uedo N

J Gastroenterol Hepatol, 2024 Jan;39(1):18-27.
PMID: 37881033 DOI: 10.1111/jgh.16383

Global warming caused by increased greenhouse gas (GHG) emissions has a direct impact on human health. Gastrointestinal (GI) endoscopy contributes significantly to GHG emissions due to energy consumption, reprocessing of endoscopes and accessories, production of equipment, safe disposal of biohazardous waste, and travel by patients. Moreover, GHGs are also generated in histopathology through tissue processing and the production of biopsy specimen bottles. The reduction in unnecessary surveillance endoscopies and biopsies is a practical approach to decrease GHG emissions without affecting disease outcomes. This narrative review explores the role of precision medicine in GI endoscopy, such as image-enhanced endoscopy and artificial intelligence, with a focus on decreasing unnecessary endoscopic procedures and biopsies in the surveillance and diagnosis of premalignant lesions in the esophagus, stomach, and colon. This review offers strategies to minimize unnecessary endoscopic procedures and biopsies, decrease GHG emissions, and maintain high-quality patient care, thereby contributing to sustainable healthcare practices.

Matched MeSH terms: Artificial Intelligence
Fulltext State-of-the-art IoV trust management a meta-synthesis systematic literature review (SLR)

Rehman A, Hassan MF, Yew KH, Paputungan I, Tran DC

PeerJ Comput Sci, 2020;6:e334.
PMID: 33816982 DOI: 10.7717/peerj-cs.334

In the near future, the Internet of Vehicles (IoV) is foreseen to become an inviolable part of smart cities. The integration of vehicular ad hoc networks (VANETs) into the IoV is being driven by the advent of the Internet of Things (IoT) and high-speed communication. However, both the technological and non-technical elements of IoV need to be standardized prior to deployment on the road. This study focuses on trust management (TM) in the IoV/VANETs/ITS (intelligent transport system). Trust has always been important in vehicular networks to ensure safety. A variety of techniques for TM and evaluation have been proposed over the years, yet few comprehensive studies that lay the foundation for the development of a "standard" for TM in IoV have been reported. The motivation behind this study is to examine all the TM models available for vehicular networks to bring together all the techniques from previous studies in this review. The study was carried out using a systematic method in which 31 papers out of 256 research publications were screened. An in-depth analysis of all the TM models was conducted and the strengths and weaknesses of each are highlighted. Considering that solutions based on AI are necessary to meet the requirements of a smart city, our second objective is to analyze the implications of incorporating an AI method based on "context awareness" in a vehicular network. It is evident from mobile ad hoc networks (MANETs) that there is potential for context awareness in ad hoc networks. The findings are expected to contribute significantly to the future formulation of IoVITS standards. In addition, gray areas and open questions for new research dimensions are highlighted.

Matched MeSH terms: Artificial Intelligence
Fulltext Development of vision based multiview gait recognition system with MMUGait database

Ng H, Tan WH, Abdullah J, Tong HL

ScientificWorldJournal, 2014;2014:376569.
PMID: 25143972 DOI: 10.1155/2014/376569

This paper describes the acquisition setup and development of a new gait database, MMUGait. This database consists of 82 subjects walking under normal condition and 19 subjects walking with 11 covariate factors, which were captured under two views. This paper also proposes a multiview model-based gait recognition system with joint detection approach that performs well under different walking trajectories and covariate factors, which include self-occluded or external occluded silhouettes. In the proposed system, the process begins by enhancing the human silhouette to remove the artifacts. Next, the width and height of the body are obtained. Subsequently, the joint angular trajectories are determined once the body joints are automatically detected. Lastly, crotch height and step-size of the walking subject are determined. The extracted features are smoothened by Gaussian filter to eliminate the effect of outliers. The extracted features are normalized with linear scaling, which is followed by feature selection prior to the classification process. The classification experiments carried out on MMUGait database were benchmarked against the SOTON Small DB from University of Southampton. Results showed correct classification rate above 90% for all the databases. The proposed approach is found to outperform other approaches on SOTON Small DB in most cases.

Matched MeSH terms: Artificial Intelligence
Fulltext Application of artificial neural network for automatic contingency analysis in power security assessment

Ismail Musirin, Titik Khawa Abdul Rahman

Scientific Research Journal, 2006;3(1):11-25.
MyJurnal

Several incidents that occurred around the world involving power failure
caused by unscheduled line outages were identified as one of the main
contributors to power failure and cascading blackout in electric power
environment. With the advancement of computer technologies, artificial
intelligence (AI) has been widely accepted as one method that can be applied
to predict the occurrence of unscheduled disturbance. This paper presents
the development of automatic contingency analysis and ranking algorithm
for the application in the Artificial Neural Network (ANN). The ANN is
developed in order to predict the post-outage severity index from a set of preoutage
data set. Data were generated using the newly developed automatic
contingency analysis and ranking (ACAR) algorithm. Tests were conducted
on the 24-bus IEEE Reliability Test Systems. Results showed that the developed
technique is feasible to be implemented practically and an agreement was
achieved in the results obtained from the tests. The developed ACAR can be
utilised for further testing and implementation in other IEEE RTS test systems
particularly in the system, which required fast computation time. On the other
hand, the developed ANN can be used for predicting the post-outage severity
index and hence system stability can be evaluated.

Matched MeSH terms: Artificial Intelligence
Fulltext Computational intelligence and metaheuristic algorithms with applications

Yang XS, Chien SF, Ting TO

ScientificWorldJournal, 2014;2014:425853.
PMID: 25610904 DOI: 10.1155/2014/425853
Matched MeSH terms: Artificial Intelligence*
Fulltext Heuristics for multiobjective optimization of two-sided assembly line systems

Jawahar N, Ponnambalam SG, Sivakumar K, Thangadurai V

ScientificWorldJournal, 2014;2014:458959.
PMID: 24790568 DOI: 10.1155/2014/458959

Products such as cars, trucks, and heavy machinery are assembled by two-sided assembly line. Assembly line balancing has significant impacts on the performance and productivity of flow line manufacturing systems and is an active research area for several decades. This paper addresses the line balancing problem of a two-sided assembly line in which the tasks are to be assigned at L side or R side or any one side (addressed as E). Two objectives, minimum number of workstations and minimum unbalance time among workstations, have been considered for balancing the assembly line. There are two approaches to solve multiobjective optimization problem: first approach combines all the objectives into a single composite function or moves all but one objective to the constraint set; second approach determines the Pareto optimal solution set. This paper proposes two heuristics to evolve optimal Pareto front for the TALBP under consideration: Enumerative Heuristic Algorithm (EHA) to handle problems of small and medium size and Simulated Annealing Algorithm (SAA) for large-sized problems. The proposed approaches are illustrated with example problems and their performances are compared with a set of test problems.

Matched MeSH terms: Artificial Intelligence*
What does ChatGPT advise about predatory publishing?

Tsigaris P, Kendall G, Teixeira da Silva JA

J Prof Nurs, 2023;49:188-189.
PMID: 38042556 DOI: 10.1016/j.profnurs.2023.08.002

The debate surrounding "predatory publishing" continues to be unable to find entirely effective solutions to dealing with this problem, despite fervent efforts by many academics and policy makers around the world. Given this situation, we were interested in appreciating whether ChatGPT would be able to offer insight and solutions, to complement current human-based efforts.

Matched MeSH terms: Artificial Intelligence*
The role of artificial intelligence in the battle against antimicrobial-resistant bacteria

Lau HJ, Lim CH, Foo SC, Tan HS

Curr Genet, 2021 Jun;67(3):421-429.
PMID: 33585980 DOI: 10.1007/s00294-021-01156-5

Antimicrobial resistance (AMR) in bacteria is a global health crisis due to the rapid emergence of multidrug-resistant bacteria and the lengthy development of new antimicrobials. In light of this, artificial intelligence in the form of machine learning has been viewed as a potential counter to delay the spread of AMR. With the aid of AI, there are possibilities to predict and identify AMR in bacteria efficiently. Furthermore, a combination of machine learning algorithms and lab testing can help to accelerate the process of discovering new antimicrobials. To date, many machine learning algorithms for antimicrobial-resistance discovery had been created and vigorously validated. Most of these algorithms produced accurate results and outperformed the traditional methods which relied on sequence comparison within a database. This mini-review will provide an updated overview of antimicrobial design workflow using the latest machine-learning antimicrobial discovery algorithms in the last 5 years. With this review, we hope to improve upon the current AMR identification and antimicrobial development techniques by introducing the use of AI into the mix, including how the algorithms could be made more effective.

Matched MeSH terms: Artificial Intelligence*
Fulltext It is Time to Incorporate Artificial Intelligence in Radiology Residency Programs

Ng KH, Tan CH

Korean J Radiol, 2023 Mar;24(3):177-179.
PMID: 36788774 DOI: 10.3348/kjr.2022.1023
Matched MeSH terms: Artificial Intelligence
Effectiveness of Create ML in microscopy image classifications: a simple and inexpensive deep learning pipeline for non-data scientists

Nagaki K, Furuta T, Yamaji N, Kuniyoshi D, Ishihara M, Kishima Y, et al.

Chromosome Res, 2021 12;29(3-4):361-371.
PMID: 34648121 DOI: 10.1007/s10577-021-09676-z

Observing chromosomes is a time-consuming and labor-intensive process, and chromosomes have been analyzed manually for many years. In the last decade, automated acquisition systems for microscopic images have advanced dramatically due to advances in their controlling computer systems, and nowadays, it is possible to automatically acquire sets of tiling-images consisting of large number, more than 1000, of images from large areas of specimens. However, there has been no simple and inexpensive system to efficiently select images containing mitotic cells among these images. In this paper, a classification system of chromosomal images by deep learning artificial intelligence (AI) that can be easily handled by non-data scientists was applied. With this system, models suitable for our own samples could be easily built on a Macintosh computer with Create ML. As examples, models constructed by learning using chromosome images derived from various plant species were able to classify images containing mitotic cells among samples from plant species not used for learning in addition to samples from the species used. The system also worked for cells in tissue sections and tetrads. Since this system is inexpensive and can be easily trained via deep learning using scientists' own samples, it can be used not only for chromosomal image analysis but also for analysis of other biology-related images.

Matched MeSH terms: Artificial Intelligence

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