Expert recommendation on collection,storage,annotation,and management of data related to medical artificial intelligence

Medical artificial intelligence(AI)and big data technology have rapidly advanced in recent years,and they are now routinely used for image-based diagnosis.China has a massive amount of medical data.However,a uniform criteria for medical data quality have yet to be established.Therefore,this review aimed to develop a standardized and detailed set of quality criteria for medical data collection,storage,annotation,and management related to medical AI.This would greatly improve the process of medical data resource sharing and the use of AI in clinical medicine.

Recommendation on data collection and annotation of ocular appearance images in ptosis

Ptosis is a common ophthalmologic condition,and the diagnosis is primarily based on ocular appearance.Thediagnosis of such conditions can be improved using emerging technology such as artificial intelligence-basedmethods.However,unified data collection and labeling standards have not yet been established.This directlyimpacts the accuracy of ptosis diagnosis based on appearance alone.Therefore,in the present study,we aimedto establish a procedure to obtain and label images to devise a recommendation system for optimal recognitionof ptosis based on ocular appearances.This would help to standardize and facilitate data sharing and serve as aguideline for the development and improvisation of algorithms in artificial intelligence for ptosis.

Expert recommendations on collection and annotation of otoscopy images for intelligent medicine

Middle and outer ear diseases are common otological diseases worldwide.Otoscopy and otoendoscopy exami-nations are essential first steps in the evaluation of patients with otological diseases.Misdiagnosis often occurs when the doctor lacks experience in interpreting the results of otoscopy or otoendoscopy,leading to delays in treatment or complications.Using deep learning to process otoscopy images and developing otoscopic artificial-intelligence-based decision-making systems will become a significant trend in the future.However,the uneven quality of otoscopy images is among the major obstacles to development of such artificial intelligence systems,and no standardized process for data acquisition,and annotation of otoscopy images in intelligent medicine has yet been fully established.The standards for data storage and data management are unified with those of other specialties and are introduced in detail here.This expert recommendation criterion improved and standardized the collection and annotation procedures for otoscopy images and fills the current gap in otologic intelligent medicine;it would thus lay a solid foundation for the standardized collection,storage,and annotation of oto-scopy images and the application of training algorithms,and promote the development of automatic diagnosis and treatment for otological diseases.The full text introduced image collection(including patient preparation,equipment standards,and image storage),image annotation standards,and quality control.

Expert recommendations on data collection and annotation of two dimensional ultrasound images in azoospermic males for evaluation of testicular spermatogenic function in intelligent medicine

Testicular two-dimensional ultrasound is a testing modality that is often used to evaluate azoospermia and other related diseases.With the continuous development of deep learning in recent years,the combination of deep learning and testicular ultrasound appears unstoppable despite a lack of relevant standards.One of the major problems associated with the digitization of ultrasound images is the uneven quality of data however,and a standardized data source and acquisition process has not yet been developed.Such a standard could fill the current gap,and establish acquisition criteria for ultrasound images of testes during the male reproductive period,including grayscale ultrasound,shear wave elastography,and contrast-enhanced ultrasound.By following these guidelines the quality of testicular ultrasound images would be improved and standardized,which would lay a solid foundation for the standardization of testicular ultrasound images,and assist automated evaluation of testicular spermatogenic function of whole testis in azoospermic males.