Optimized artificial intelligence for enhanced ectasia detection using Scheimpflug-based corneal tomography and biomechanical data

PurposeTo optimize artificial intelligence (AI) algorithms to integrate Scheimpflug-based corneal tomography and biomechanics to enhance ectasia detection.DesignMulticenter cross-sectional case-control retrospective study.Methods3,886 unoperated eyes from 3,412 patients had Pentacam and Corvis ST (Oculus Optikgeräte GmbH; Wetzlar, Germany) examinations. The database included one eye randomly selected from 1,680 normal patients (N), and from 1,181 "bilateral" keratoconus (KC) patients, along with 551 normal topography eyes from very asymmetric ectasia patients (VAE-NT), and their 474 unoperated ectatic (VAE-E) eyes. The current TBIv1 (tomographic-biomechanical index) was tested, and an optimized AI algorithm was developed for augmenting accuracy.ResultsThe area under the receiver operating characteristic curve (AUC) of the TBIv1 for discriminating clinical ectasia (KC and VAE-E) was 0.999 (98.5% sensitivity; 98.6% specificity [cutoff 0.5]), and for VAE-NT, 0.899 (76% sensitivity; 89.1% specificity [cutoff 0.29]). A novel random forest algorithm (TBIv2), developed with 18 features in 156 trees using 10-fold cross-validation, had significantly higher AUC (0.945; DeLong, pConclusionAI optimization to integrate Scheimpflug-based corneal tomography and biomechanical assessments augments accuracy for ectasia detection, characterizing ectasia susceptibility in the diverse VAE-NT group. Some VAE patients may be true unilateral ectasia. Machine learning considering additional data, including epithelial thickness or other parameters from multimodal refractive imaging, will continuously enhance accuracy

Feathermoss and epiphytic Nostoc cooperate differently: expanding the spectrum of plant-cyanobacteria symbiosis.

Dinitrogen (N2)-fixation by cyanobacteria in symbiosis with feathermosses is the primary pathway of biological nitrogen (N) input into boreal forests. Despite its significance, little is known about the cyanobacterial gene repertoire and regulatory rewiring needed for the establishment and maintenance of the symbiosis. To determine gene acquisitions and regulatory changes allowing cyanobacteria to form and maintain this symbiosis, we compared genomically closely related symbiotic-competent and -incompetent Nostoc strains using a proteogenomics approach and an experimental set up allowing for controlled chemical and physical contact between partners. Thirty-two gene families were found only in the genomes of symbiotic strains, including some never before associated with cyanobacterial symbiosis. We identified conserved orthologs that were differentially expressed in symbiotic strains, including protein families involved in chemotaxis and motility, NO regulation, sulfate/phosphate transport, and glycosyl-modifying and oxidative stress-mediating exoenzymes. The physical moss-cyanobacteria epiphytic symbiosis is distinct from other cyanobacteria-plant symbioses, with Nostoc retaining motility, and lacking modulation of N2-fixation, photosynthesis, GS-GOGAT cycle and heterocyst formation. The results expand our knowledge base of plant-cyanobacterial symbioses, provide a model of information and material exchange in this ecologically significant symbiosis, and suggest new currencies, namely nitric oxide and aliphatic sulfonates, may be involved in establishing and maintaining the cyanobacteria-feathermoss symbiosis