Application of artificial intelligence in the diagnosis and treatment of Kawasaki disease

This editorial provides commentary on an article titled"Potential and limitationsof ChatGPT and generative artificial intelligence(AI)in medical safety education"recently published in the World Journal of Clinical Cases.AI has enormous potentialfor various applications in the field of Kawasaki disease(KD).One is machinelearning(ML)to assist in the diagnosis of KD,and clinical prediction models havebeen constructed worldwide using ML;the second is using a gene signalcalculation toolbox to identify KD,which can be used to monitor key clinicalfeatures and laboratory parameters of disease severity;and the third is using deeplearning(DL)to assist in cardiac ultrasound detection.The performance of the DLalgorithm is similar to that of experienced cardiac experts in detecting coronaryartery lesions to promoting the diagnosis of KD.To effectively utilize AI in thediagnosis and treatment process of KD,it is crucial to improve the accuracy of AIdecision-making using more medical data,while addressing issues related topatient personal information protection and AI decision-making responsibility.AIprogress is expected to provide patients with accurate and effective medicalservices that will positively impact the diagnosis and treatment of KD in thefuture.

Reflection on the equitable attribution of responsibility for artificial intelligence-assisted diagnosis and treatment decisions

Artificial intelligence(AI)is developing rapidly and is being used in several medical capacities,including assisting in diagnosis and treatment decisions.As a result,this raises the conceptual and practical problem of how to distribute responsibility when AI-assisted diagnosis and treatment have been used and patients are harmed in the process.Regulations on this issue have not yet been established.It would be beneficial to tackle responsibility attribution prior to the development of biomedical AI technologies and ethical guidelines.In general,human doctors acting as superiors need to bear responsibility for their clinical decisions.However,human doctors should not bear responsibility for the behavior of an AI doctor that is practicing medicine inde-pendently.According to the degree of fault-which includes internal institutional ethics,the AI bidding process in procurement,and the medical process-clinical institutions are required to bear corresponding responsibility.AI manufacturers are responsible for creating accurate algorithms,network security,and insuring patient privacy protection.However,the AI itself should not be subjected to legal evaluation since there is no need for it to bear responsibility.Corresponding responsibility should be borne by the employer,in this case the medical institution.

Artificial Intelligence-Driven Vehicle Fault Diagnosis to Revolutionize Automotive Maintenance:A Review

Conventional fault diagnosis systems have constrained the automotive industry to damage vehicle maintenance and component longevity critically.Hence,there is a growing demand for advanced fault diagnosis technologies to mitigate the impact of these limitations on unplanned vehicular downtime caused by unanticipated vehicle break-downs.Due to vehicles’increasingly complex and autonomous nature,there is a growing urgency to investigate novel diagnosis methodologies for improving safety,reliability,and maintainability.While Artificial Intelligence(AI)has provided a great opportunity in this area,a systematic review of the feasibility and application of AI for Vehicle Fault Diagnosis(VFD)systems is unavailable.Therefore,this review brings new insights into the potential of AI in VFD methodologies and offers a broad analysis using multiple techniques.We focus on reviewing relevant literature in the field of machine learning as well as deep learning algorithms for fault diagnosis in engines,lifting systems(suspensions and tires),gearboxes,and brakes,among other vehicular subsystems.We then delve into some examples of the use of AI in fault diagnosis and maintenance for electric vehicles and autonomous cars.The review elucidates the transformation of VFD systems that consequently increase accuracy,economization,and prediction in most vehicular sub-systems due to AI applications.Indeed,the limited performance of systems based on only one of these AI techniques is likely to be addressed by combinations:The integration shows that a single technique or method fails its expectations,which can lead to more reliable and versatile diagnostic support.By synthesizing current information and distinguishing forthcoming patterns,this work aims to accelerate advancement in smart automotive innovations,conforming with the requests of Industry 4.0 and adding to the progression of more secure,more dependable vehicles.The findings underscored the necessity for cross-disciplinary cooperation and examined the total potential of AI in vehicle default analysis.

The enlightenment of artificial intelligence large-scale model on the research of intelligent eye diagnosis in traditional Chinese medicine

Eye diagnosis is a method for inspecting systemic diseases and syndromes by observing the eyes.With the development of intelligent diagnosis in traditional Chinese medicine(TCM);artificial intelligence(AI)can improve the accuracy and efficiency of eye diagnosis.However;the research on intelligent eye diagnosis still faces many challenges;including the lack of standardized and precisely labeled data;multi-modal information analysis;and artificial in-telligence models for syndrome differentiation.The widespread application of AI models in medicine provides new insights and opportunities for the research of eye diagnosis intelli-gence.This study elaborates on the three key technologies of AI models in the intelligent ap-plication of TCM eye diagnosis;and explores the implications for the research of eye diagno-sis intelligence.First;a database concerning eye diagnosis was established based on self-su-pervised learning so as to solve the issues related to the lack of standardized and precisely la-beled data.Next;the cross-modal understanding and generation of deep neural network models to address the problem of lacking multi-modal information analysis.Last;the build-ing of data-driven models for eye diagnosis to tackle the issue of the absence of syndrome dif-ferentiation models.In summary;research on intelligent eye diagnosis has great potential to be applied the surge of AI model applications.

Artificial intelligence for characterization of diminutive colorectal polyps:A feasibility study comparing two computer-aided diagnosis systems

BACKGROUND Artificial intelligence(AI)has potential in the optical diagnosis of colorectal polyps.AIM To evaluate the feasibility of the real-time use of the computer-aided diagnosis system(CADx)AI for ColoRectal Polyps(AI4CRP)for the optical diagnosis of diminutive colorectal polyps and to compare the performance with CAD EYE^(TM)(Fujifilm,Tokyo,Japan).CADx influence on the optical diagnosis of an expert endoscopist was also investigated.METHODS AI4CRP was developed in-house and CAD EYE was proprietary software provided by Fujifilm.Both CADxsystems exploit convolutional neural networks.Colorectal polyps were characterized as benign or premalignant and histopathology was used as gold standard.AI4CRP provided an objective assessment of its characterization by presenting a calibrated confidence characterization value(range 0.0-1.0).A predefined cut-off value of 0.6 was set with values<0.6 indicating benign and values≥0.6 indicating premalignant colorectal polyps.Low confidence characterizations were defined as values 40%around the cut-off value of 0.6(<0.36 and>0.76).Self-critical AI4CRP’s diagnostic performances excluded low confidence characterizations.RESULTS AI4CRP use was feasible and performed on 30 patients with 51 colorectal polyps.Self-critical AI4CRP,excluding 14 low confidence characterizations[27.5%(14/51)],had a diagnostic accuracy of 89.2%,sensitivity of 89.7%,and specificity of 87.5%,which was higher compared to AI4CRP.CAD EYE had a 83.7%diagnostic accuracy,74.2%sensitivity,and 100.0%specificity.Diagnostic performances of the endoscopist alone(before AI)increased nonsignificantly after reviewing the CADx characterizations of both AI4CRP and CAD EYE(AI-assisted endoscopist).Diagnostic performances of the AI-assisted endoscopist were higher compared to both CADx-systems,except for specificity for which CAD EYE performed best.CONCLUSION Real-time use of AI4CRP was feasible.Objective confidence values provided by a CADx is novel and self-critical AI4CRP showed higher diagnostic performances compared to AI4CRP.

Application of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma: A review

Although artificial intelligence(AI)was initially developed many years ago,it has experienced spectacular advances over the last 10 years for application in the field of medicine,and is now used for diagnostic,therapeutic and prognostic purposes in almost all fields.Its application in the area of hepatology is especially relevant for the study of hepatocellular carcinoma(HCC),as this is a very common tumor,with particular radiological characteristics that allow its diagnosis without the need for a histological study.However,the interpretation and analysis of the resulting images is not always easy,in addition to which the images vary during the course of the disease,and prognosis and treatment response can be conditioned by multiple factors.The vast amount of data available lend themselves to study and analysis by AI in its various branches,such as deeplearning(DL)and machine learning(ML),which play a fundamental role in decision-making as well as overcoming the constraints involved in human evaluation.ML is a form of AI based on automated learning from a set of previously provided data and training in algorithms to organize and recognize patterns.DL is a more extensive form of learning that attempts to simulate the working of the human brain,using a lot more data and more complex algorithms.This review specifies the type of AI used by the various authors.However,welldesigned prospective studies are needed in order to avoid as far as possible any bias that may later affect the interpretability of the images and thereby limit the acceptance and application of these models in clinical practice.In addition,professionals now need to understand the true usefulness of these techniques,as well as their associated strengths and limitations.

Artificial intelligence-assisted esophageal cancer management:Now and future

Esophageal cancer poses diagnostic,therapeutic and economic burdens in highrisk regions.Artificial intelligence(AI)has been developed for diagnosis and outcome prediction using various features,including clinicopathologic,radiologic,and genetic variables,which can achieve inspiring results.One of the most recent tasks of AI is to use state-of-the-art deep learning technique to detect both early esophageal squamous cell carcinoma and esophageal adenocarcinoma in Barrett’s esophagus.In this review,we aim to provide a comprehensive overview of the ways in which AI may help physicians diagnose advanced cancer and make clinical decisions based on predicted outcomes,and combine the endoscopic images to detect precancerous lesions or early cancer.Pertinent studies conducted in recent two years have surged in numbers,with large datasets and external validation from multi-centers,and have partly achieved intriguing results of expert’s performance of AI in real time.Improved pre-trained computer-aided diagnosis algorithms in the future studies with larger training and external validation datasets,aiming at real-time video processing,are imperative to produce a diagnostic efficacy similar to or even superior to experienced endoscopists.Meanwhile,supervised randomized controlled trials in real clinical practice are highly essential for a solid conclusion,which meets patient-centered satisfaction.Notably,ethical and legal issues regarding the blackbox nature of computer algorithms should be addressed,for both clinicians and regulators.

Development of artificial intelligence technology in diagnosis,treatment,and prognosis of colorectal cancer

Artificial intelligence(AI)technology has made leaps and bounds since its invention.AI technology can be subdivided into many technologies such as machine learning and deep learning.The application scope and prospect of different technologies are also totally different.Currently,AI technologies play a pivotal role in the highly complex and wide-ranging medical field,such as medical image recognition,biotechnology,auxiliary diagnosis,drug research and development,and nutrition.Colorectal cancer(CRC)is a common gastrointestinal cancer that has a high mortality,posing a serious threat to human health.Many CRCs are caused by the malignant transformation of colorectal polyps.Therefore,early diagnosis and treatment are crucial to CRC prognosis.The methods of diagnosing CRC are divided into imaging diagnosis,endoscopy,and pathology diagnosis.Treatment methods are divided into endoscopic treatment,surgical treatment,and drug treatment.AI technology is in the weak era and does not have communication capabilities.Therefore,the current AI technology is mainly used for image recognition and auxiliary analysis without in-depth communication with patients.This article reviews the application of AI in the diagnosis,treatment,and prognosis of CRC and provides the prospects for the broader application of AI in CRC.

Artificial intelligence assisted pterygium diagnosis:current status and perspectives

Pterygium is a prevalent ocular disease that can cause discomfort and vision impairment.Early and accurate diagnosis is essential for effective management.Recently,artificial intelligence(AI)has shown promising potential in assisting clinicians with pterygium diagnosis.This paper provides an overview of AI-assisted pterygium diagnosis,including the AI techniques used such as machine learning,deep learning,and computer vision.Furthermore,recent studies that have evaluated the diagnostic performance of AI-based systems for pterygium detection,classification and segmentation were summarized.The advantages and limitations of AI-assisted pterygium diagnosis and discuss potential future developments in this field were also analyzed.The review aims to provide insights into the current state-of-the-art of AI and its potential applications in pterygium diagnosis,which may facilitate the development of more efficient and accurate diagnostic tools for this common ocular disease.

Application of an artificial intelligence system for endoscopic diagnosis of superficial esophageal squamous cell carcinoma

BACKGROUND Upper gastrointestinal endoscopy is critical for esophageal squamous cell carcinoma(ESCC)detection;however,endoscopists require long-term training to avoid missing superficial lesions.AIM To develop a deep learning computer-assisted diagnosis(CAD)system for endoscopic detection of superficial ESCC and investigate its application value.METHODS We configured the CAD system for white-light and narrow-band imaging modes based on the YOLO v5 algorithm.A total of 4447 images from 837 patients and 1695 images from 323 patients were included in the training and testing datasets,respectively.Two experts and two non-expert endoscopists reviewed the testing dataset independently and with computer assistance.The diagnostic performance was evaluated in terms of the area under the receiver operating characteristic curve,accuracy,sensitivity,and specificity.RESULTS The area under the receiver operating characteristics curve,accuracy,sensitivity,and specificity of the CAD system were 0.982[95%confidence interval(CI):0.969-0.994],92.9%(95%CI:89.5%-95.2%),91.9%(95%CI:87.4%-94.9%),and 94.7%(95%CI:89.0%-97.6%),respectively.The accuracy of CAD was significantly higher than that of non-expert endoscopists(78.3%,P<0.001 compared with CAD)and comparable to that of expert endoscopists(91.0%,P=0.129 compared with CAD).After referring to the CAD results,the accuracy of the non-expert endoscopists significantly improved(88.2%vs 78.3%,P<0.001).Lesions with Paris classification type 0-IIb were more likely to be inaccurately identified by the CAD system.CONCLUSION The diagnostic performance of the CAD system is promising and may assist in improving detectability,particularly for inexperienced endoscopists.

Fault Detection and Diagnosis of a Gearbox in Marine Propulsion Systems Using Bispectrum Analysis and Artificial Neural Networks

A marine propulsion system is a very complicated system composed of many mechanical components.As a result,the vibration signal of a gearbox in the system is strongly coupled with the vibration signatures of other components including a diesel engine and main shaft.It is therefore imperative to assess the coupling effect on diagnostic reliability in the process of gear fault diagnosis.For this reason,a fault detection and diagnosis method based on bispectrum analysis and artificial neural networks (ANNs) was proposed for the gearbox with consideration given to the impact of the other components in marine propulsion systems.To monitor the gear conditions,the bispectrum analysis was first employed to detect gear faults.The amplitude-frequency plots containing gear characteristic signals were then attained based on the bispectrum technique,which could be regarded as an index actualizing forepart gear faults diagnosis.Both the back propagation neural network (BPNN) and the radial-basis function neural network (RBFNN) were applied to identify the states of the gearbox.The numeric and experimental test results show the bispectral patterns of varying gear fault severities are different so that distinct fault features of the vibrant signal of a marine gearbox can be extracted effectively using the bispectrum,and the ANN classification method has achieved high detection accuracy.Hence,the proposed diagnostic techniques have the capability of diagnosing marine gear faults in the earlier phases,and thus have application importance.

Research progress in artificial intelligence assisted diabetic retinopathy diagnosis

Diabetic retinopathy(DR)is one of the most common retinal vascular diseases and one of the main causes of blindness worldwide.Early detection and treatment can effectively delay vision decline and even blindness in patients with DR.In recent years,artificial intelligence(AI)models constructed by machine learning and deep learning(DL)algorithms have been widely used in ophthalmology research,especially in diagnosing and treating ophthalmic diseases,particularly DR.Regarding DR,AI has mainly been used in its diagnosis,grading,and lesion recognition and segmentation,and good research and application results have been achieved.This study summarizes the research progress in AI models based on machine learning and DL algorithms for DR diagnosis and discusses some limitations and challenges in AI research.

Beyond images: Emerging role of Raman spectroscopy-based artificial intelligence in diagnosis of gastric neoplasia

White-light endoscopy with tissue biopsy is the gold standard interface for diagnosing gastric neoplastic lesions.However, misdiagnosis of lesions is a challenge because of operator variability and learning curve issues. These issues have not been resolved despite the introduction of advanced imaging technologies, including narrow band imaging, and confocal laser endomicroscopy. To ensure consistently high diagnostic accuracy among endoscopists,artificial intelligence(AI) has recently been introduced to assist endoscopists in the diagnosis of gastric neoplasia.Current endoscopic AI systems for endoscopic diagnosis are mostly based upon interpretation of endoscopic images. In real-life application, the image-based AI system remains reliant upon skilful operators who will need to capture sufficiently good quality images for the AI system to analyze. Such an ideal situation may not always be possible in routine practice. In contrast, non-image-based AI is less constraint by these requirements. Our group has recently developed an endoscopic Raman fibre-optic probe that can be delivered into the gastrointestinal tract via the working channel of any endoscopy for Raman measurements. We have also successfully incorporated the endoscopic Raman spectroscopic system with an AI system. Proof of effectiveness has been demonstrated in in vivo studies using the Raman endoscopic system in close to 1,000 patients. The system was able to classify normal gastric tissue, gastric intestinal metaplasia, gastric dysplasia and gastric cancer, with diagnostic accuracy of >85%. Because of the excellent correlation between Raman spectra and histopathology, the Raman-AI system can provide optical diagnosis, thus allowing the endoscopists to make clinical decisions on the spot. Furthermore, by allowing nonexpert endoscopists to make real-time decisions as well as expert endoscopists, the system will enable consistency of care.

Artificial intelligence-assisted endoscopic detection of esophageal neoplasia in early stage:The next step?

Esophageal cancer(EC)is a common malignant tumor of the digestive tract and originates from the epithelium of the esophageal mucosa.It has been confirmed that early EC lesions can be cured by endoscopic therapy,and the curative effect is equivalent to that of surgical operation.Upper gastrointestinal endoscopy is still the gold standard for EC diagnosis.The accuracy of endoscopic examination results largely depends on the professional level of the examiner.Artificial intelligence(AI)has been applied in the screening of early EC and has shown advantages;notably,it is more accurate than less-experienced endoscopists.This paper reviews the application of AI in the field of endoscopic detection of early EC,including squamous cell carcinoma and adenocarcinoma,and describes the relevant progress.Although up to now most of the studies evaluating the clinical application of AI in early EC endoscopic detection are focused on still images,AIassisted real-time detection based on live-stream video may be the next step.

Adoption of Artificial Intelligence for Diagnosis and Treatment of <i>Staphylococcus aureus</i>Infections Disease on Humans

The aim of this paper is to develop an expert system that could aid medical practitioner to effectively diagnose and treat Staphylococcus aureus infections disease on a human. The objective of the research includes to develop an expert system for quick diagnosis and detection of Staphylococcus aureus bacteria on human skin, a system that aids in accurate treatment of staph infectious diseases by doctors, helps in quick decision making in the hospital, improves accuracy in drug prescription, and a system that will bring about computerized storage process, and to enlighten the knowledge workers on how to implement a computer based decision support systems and importance of it in the health care. The research was motivated due to delay in diagnosis and identification of Staphylococcus aureus bacteria and the fast rate at which infectious disease is spreading, delay in treatment of these bacteria, increase of guess work by health practitioners leading to delay in decision making and lack of electronic storage facility in the hospitals. Top down approach was used in the system design of this research while adopting expert system as the methodology and the programming language used was Java and database design used was MySQL. The result after design was a computerized standalone application that assists health practitioners (Doctor’s) in quick identification, diagnosis, prescription and treatment of Staphylococcus aureus bacteria on human skin. The expert system will facilitate quick decision making in the clinic.

Role of artificial intelligence in the diagnosis of oesophageal neoplasia:2020 an endoscopic odyssey

The past decade has seen significant advances in endoscopic imaging and optical enhancements to aid early diagnosis.There is still a treatment gap due to the underdiagnosis of lesions of the oesophagus.Computer aided diagnosis may play an important role in the coming years in providing an adjunct to endoscopists in the early detection and diagnosis of early oesophageal cancers,therefore curative endoscopic therapy can be offered.Research in this area of artificial intelligence is expanding and the future looks promising.In this review article we will review current advances in artificial intelligence in the oesophagus and future directions for development.

Application of artificial intelligence in tongue diagnosis of traditional Chinese medicine:A review

Tongue diagnosis is an important process to non-invasively assess the condition of a patient’s internal organs in traditional Chinese medicine(TCM)and each part of the tongue is related to corresponding internal organs.Due to continuing computer technological advances,especially the artificial intelligence(AI)methods have achieved significant success in tackling tongue image acquisition,processing,and classification,novel AI methods are being introduced in traditional Chinese medicine tongue diagnosis medical practices.Traditional tongue diagnose depends on observations of tongue characteristics,such as color,shape,texture,moisture,etc.by traditional Chinese medicine physicians.The appearance of the tongue color,texture and coating reflects the improvement or deterioration of patient’s conditions.Moreover,AI can now distinguish patient’s condition through tongue images,texture or coating,which is all possible increasingly with help from traditional Chinese medicine physicians under the traditional Chinese medicine tongue theory.AI has enabled humans to do what was previously unimagined:traditional Chinese medicine tongue diagnosis with feeding a large amount of tongue image and tongue texture/coating data to train the AI modes.This review focuses on the research advances of AI in TCM tongue diagnosis thus far to identify the major scientific methods and prospects.In this article,we tried to review the AI application in resolving the tongue diagnosis of traditional Chinese medicine on color correction,tongue image extraction,tongue texture/coating segmentation.

Fault-Diagnosis Method Based on Support Vector Machine and Artificial Immune for Batch Process

A new fault-diagnosis method to be used in batch processes based on multi-phase regression is presented to overcome the difficulty arising in the processes due to non-uniform sample data in each phase.Support vector machine is first used for phase identification,and for each phase,improved artificial immune network is developed to analyze and recognize fault patterns.A new cell elimination role is proposed to enhance the incremental clustering capability of the immune network.The proposed method has been applied to glutamic acid fermentation,comparison results have indicated that the proposed approach can better classify fault samples and yield higher diagnosis precision.

Application of Artificial Neural Networks in Sonic Diagnosis of Cracking Hammer with Artificial Diamond

On the basis of the characteristic parameters selected from the fault sonic signals of cracking hammer with artificial diamond,by means of with time series analysis and time domain statistics,three layer artificial neural network is trained by an improved BP algorithm.The results state that the fault sonic signals can be identified by trained network system precisely.

Artificial Neural Network Applied to Quality Diagnosis

In this paper, we first make a brief review on the fundamental properties of artificial neural networks (ANN) and the basic models, and explore emphatically some potential application of artificial neural networks in the area of product quality diagnosis, prediction and control, state supervision and classification, factor recognition, and expert system based diagnosis, then set up the ANN models and expert system for quality forecasting, monitoring and diagnosing. We point out that combining ANN with other techniques will have the broad development and application of perspectives. Finally, the paper gives out some practical applications for the models and the system.