J Am Med Inform Assoc - Automatic glaucoma diagnosis through medical imaging informatics.

Tópicos

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Resumo

CKGROUND: Computer-aided diagnosis for screening utilizes computer-based analytical methodologies to process patient information. Glaucoma is the leading irreversible cause of blindness. Due to the lack of an effective and standard screening practice, more than 50% of the cases are undiagnosed, which prevents the early treatment of the disease.OBJECTIVE: To design an automatic glaucoma diagnosis architecture automatic glaucoma diagnosis through medical imaging informatics (AGLAIA-MII) that combines patient personal data, medical retinal fundus image, and patient's genome information for screening.MATERIALS AND METHODS: 2258 cases from a population study were used to evaluate the screening software. These cases were attributed with patient personal data, retinal images and quality controlled genome data. Utilizing the multiple kernel learning-based classifier, AGLAIA-MII, combined patient personal data, major image features, and important genome single nucleotide polymorphism (SNP) features.RESULTS AND DISCUSSION: Receiver operating characteristic curves were plotted to compare AGLAIA-MII's performance with classifiers using patient personal data, images, and genome SNP separately. AGLAIA-MII was able to achieve an area under curve value of 0.866, better than 0.551, 0.722 and 0.810 by the individual personal data, image and genome information components, respectively. AGLAIA-MII also demonstrated a substantial improvement over the current glaucoma screening approach based on intraocular pressure.CONCLUSIONS: AGLAIA-MII demonstrates for the first time the capability of integrating patients' personal data, medical retinal image and genome information for automatic glaucoma diagnosis and screening in a large dataset from a population study. It paves the way for a holistic approach for automatic objective glaucoma diagnosis and screening.

Resumo Limpo

ckground computeraid diagnosi screen util computerbas analyt methodolog process patient inform glaucoma lead irrevers caus blind due lack effect standard screen practic case undiagnos prevent earli treatment diseaseobject design automat glaucoma diagnosi architectur automat glaucoma diagnosi medic imag informat aglaiamii combin patient person data medic retin fundus imag patient genom inform screeningmateri method case popul studi use evalu screen softwar case attribut patient person data retin imag qualiti control genom data util multipl kernel learningbas classifi aglaiamii combin patient person data major imag featur import genom singl nucleotid polymorph snp featuresresult discuss receiv oper characterist curv plot compar aglaiamii perform classifi use patient person data imag genom snp separ aglaiamii abl achiev area curv valu better individu person data imag genom inform compon respect aglaiamii also demonstr substanti improv current glaucoma screen approach base intraocular pressureconclus aglaiamii demonstr first time capabl integr patient person data medic retin imag genom inform automat glaucoma diagnosi screen larg dataset popul studi pave way holist approach automat object glaucoma diagnosi screen

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