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1.
Front Med (Lausanne) ; 8: 713284, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34722561

RESUMO

Purpose: To broaden the mutation and phenotype spectrum of the GJA8 and CHMP4B genes and to reveal genotype-phenotype correlations in a cohort of Chinese patients with congenital cataracts (CCs). Methods: Six Chinese Han families with CCs inherited in an autosomal dominant (AD) pattern were recruited for this study. All patients underwent full ocular examinations. Genomic DNA was extracted from the leukocytes of peripheral blood collected from all available patients and their unaffected family members. Whole-exome sequencing (WES) was performed on all probands and at least one of their parents. Candidate variants were further confirmed by Sanger sequencing. Bioinformatic analysis with several computational predictive programs was performed to assess the impacts of the candidate variants on the structure and function of the proteins. Results: Four heterozygous candidate variants in three different genes (CRYBB2, GJA8, and CHMP4B) were identified in affected individuals from the six families, including two novel missense variants (GJA8: c.64G > C/p. G22R, and CHMP4B: c.587C > G/p. S196C), one missense mutation (CRYBB2: c.562C > T/p. R188C), and one small deletion (GJA8: c.426_440delGCTGGAGGGGACCCT/p.143_147delLEGTL). The three missense mutations were predicted as deleterious in all four computational prediction programs. In the homologous model, the GJA8: p.143_147delLEGTL mutation showed a sequence deletion of five amino acids at the cytoplasmic loop of the Cx50 protein, close to the third transmembrane domain. Patients carrying mutations in the same gene showed similar cataract phenotypes at a young age, including total cataracts, Y-sutural with fetal nuclear cataracts, and subcapsular cataracts. Conclusion: This study further expands the mutation spectrum and genotype-phenotype correlation of CRYBB2, GJA8, and CHMP4B underlying CCs. This study sheds light on the importance of comparing congenital cataract phenotypes in patients at the same age stage. It offers clues for the pathogenesis of CCs and allows for an early prenatal diagnosis for families carrying these genetic variants.

2.
Front Bioeng Biotechnol ; 9: 651340, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34805102

RESUMO

Subretinal fluid (SRF) can lead to irreversible visual loss in patients with central serous chorioretinopathy (CSC) if not absorbed in time. Early detection and intervention of SRF can help improve visual prognosis and reduce irreversible damage to the retina. As fundus image is the most commonly used and easily obtained examination for patients with CSC, the purpose of our research is to investigate whether and to what extent SRF depicted on fundus images can be assessed using deep learning technology. In this study, we developed a cascaded deep learning system based on fundus image for automated SRF detection and macula-on/off serous retinal detachment discerning. The performance of our system is reliable, and its accuracy of SRF detection is higher than that of experienced retinal specialists. In addition, the system can automatically indicate whether the SRF progression involves the macula to provide guidance of urgency for patients. The implementation of our deep learning system could effectively reduce the extent of vision impairment resulting from SRF in patients with CSC by providing timely identification and referral.

3.
Am J Ophthalmol ; 2021 Oct 12.
Artigo em Inglês | MEDLINE | ID: mdl-34653355

RESUMO

PURPOSE: To compare outcomes of in-the-bag versus ciliary sulcus secondary intraocular lens (IOL) implantation for pediatric aphakia. DESIGN: Prospective interventional case series. METHODS: SETTING: institutional. PATIENT: Two hundred and two children (355 aphakic eyes) diagnosed as congenital cataracts and undergoing cataract extraction before 24 months of age. INTERVENTION: Pediatric aphakic eyes underwent either in-the-bag or ciliary sulcus secondary IOL implantation according to the amount of residual lens capsule and were followed for three years postoperatively. MAIN OUTCOME MEASURES: Adverse events (AEs), IOL tilt and decentration, best corrected visual acuity (BCVA) in operative eye. RESULTS: One hundred forty-four eyes (40.6%, 89 children) received in-the-bag IOL implantation (capsular group) and 211 (59.4%, 132 children) underwent ciliary sulcus IOL implantation (sulcus group). Kaplan-Meier curves showed that the time-dependent incidence of glaucoma-related adverse events (GRAEs) (P=0.005) and any AEs (P=0.002) were higher in the sulcus group. In-the-bag IOL implantation was a strong protective factor against GRAE (HR, 0.08, 95CI:0.01∼0.53; P=0.009) and any AEs (HR, 0.21 95CI: 0.08∼0.57; P=0.002). Clinically significant IOL decentration (>0.4mm) was more common in the sulcus group compared to the capsular group (vertical decentration: 29.8% vs. 15.7%, P=0.005; horizontal decentration: 30.3% vs. 9.35%, P<0.001). BCVA in the capsular group was better than that in the sulcus group (logarithm of the minimum angle of resolution [LogMAR] 0.56 vs. 0.67, P=0.014). CONCLUSIONS: Compared to ciliary sulcus secondary IOL implantation, in-the-bag IOL implantation reduced AEs, and yielded better IOL centration and BCVA for pediatric aphakia.

4.
Front Bioeng Biotechnol ; 9: 657866, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34513804

RESUMO

Reliable validated methods are necessary to verify the performance of diagnosis and therapy-assisted models in clinical practice. However, some validated results have research bias and may not reflect the results of real-world application. In addition, the conduct of clinical trials has executive risks for the indeterminate effectiveness of models and it is challenging to finish validated clinical trials of rare diseases. Real world data (RWD) can probably solve this problem. In our study, we collected RWD from 251 patients with a rare disease, childhood cataract (CC) and conducted a retrospective study to validate the CC surgical decision model. The consistency of the real surgical type and recommended surgical type was 94.16%. In the cataract extraction (CE) group, the model recommended the same surgical type for 84.48% of eyes, but the model advised conducting cataract extraction and primary intraocular lens implantation (CE + IOL) surgery in 15.52% of eyes, which was different from the real-world choices. In the CE + IOL group, the model recommended the same surgical type for 100% of eyes. The real-recommended matched rates were 94.22% in the eyes of bilateral patients and 90.38% in the eyes of unilateral patients. Our study is the first to apply RWD to complete a retrospective study evaluating a clinical model, and the results indicate the availability and feasibility of applying RWD in model validation and serve guidance for intelligent model evaluation for rare diseases.

5.
J Natl Cancer Inst ; 2021 Sep 02.
Artigo em Inglês | MEDLINE | ID: mdl-34473310

RESUMO

BACKGROUND: Cystoscopy plays an important role in bladder cancer (BCa) diagnosis and treatment, but its sensitivity needs improvement. Artificial intelligence has shown promise in endoscopy, but few cystoscopic applications have been reported. We report a Cystoscopy Artificial Intelligence Diagnostic System (CAIDS) for BCa diagnosis. METHODS: In total, 69,204 images from 10,729 consecutive patients from six hospitals were collected and divided into training, internal validation, and external validation sets. The CAIDS was built using a pyramid scene parsing network and transfer learning. A subset (n = 260) of the validation sets was used for a performance comparison between the CAIDS and urologists for complex lesion detection. The diagnostic accuracy, sensitivity, specificity, and positive and negative predictive values and 95% confidence intervals (CIs) were calculated using the Clopper-Pearson method. RESULTS: The diagnostic accuracies of the CAIDS were 0.977 (95% CI = 0.974-0.979) in the internal validation set and 0.990 (95% CI = 0.979-0.996), 0.982 (95% CI = 0.974-0.988), 0.978 (95% CI = 0.959-0.989), and 0.991 (95% CI = 0.987-0.994) in different external validation sets. In the CAIDS versus urologists' comparisons, the CAIDS showed high accuracy and sensitivity (accuracy = 0.939, 95% CI = 0.902-0.964; and sensitivity = 0.954, 95% CI = 0.902-0.983) with a short latency of 12 s, much more accurate and quicker than the expert urologists. CONCLUSIONS: The CAIDS achieved accurate BCa detection with a short latency. The CAIDS may provide many clinical benefits, from increasing the diagnostic accuracy for BCa, even for commonly misdiagnosed cases such as flat cancerous tissue (carcinoma in situ), to reducing the operation time for cystoscopy.

6.
Artigo em Inglês | MEDLINE | ID: mdl-34338234

RESUMO

PURPOSE: To study the morphology of the posterior lens cortex and posterior capsules (PCs) in pediatric patients with posterior lens opacities using intraoperative optical coherence tomography (iOCT). SETTING: Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China. DESIGN: A prospective observational study. METHODS: Pediatric patients with posterior lens opacities were imaged using iOCT during cataract surgery. The morphology of the posterior lens cortex and PC, along with the common patterns to indicate PC integrity, were assessed. Moreover, posterior capsule rent during surgery was observed. RESULTS: A total of 62 eyes from 53 patients were included. The mean age of patients was 3.8 years. Four morphological variants of posterior lens opacity were observed: Type I (54.8%, 34/62) with intact PC; Type II (32.3%, 20/62) with intact PC, which protruded into the anterior vitreous; Type III (4.8%, 3/62) with deficient PC and an inability to delineate PC and type IV (8.1%, 5/62) with dense opacity and an inability to characterize the posterior cortex and PC. Phacoemulsification could be performed in types I and II. In types III and IV, manual nucleus removal was performed instead of phacoemulsification. Three cases (100%) of type III PC dehiscence developed during surgery, while no cases developed PC dehiscence of other types. CONCLUSION: The morphology of the PC and posterior lens cortex in pediatric posterior lens opacities could be categorized and PC integrity could be assessed using iOCT, which was useful to guide surgical strategies and increase safety in preexisting posterior capsular dehiscence in pediatric cataract surgery.

8.
Eye (Lond) ; 2021 Aug 03.
Artigo em Inglês | MEDLINE | ID: mdl-34345030

RESUMO

BACKGROUND: Retinal exudates and/or drusen (RED) can be signs of many fundus diseases that can lead to irreversible vision loss. Early detection and treatment of these diseases are critical for improving vision prognosis. However, manual RED screening on a large scale is time-consuming and labour-intensive. Here, we aim to develop and assess a deep learning system for automated detection of RED using ultra-widefield fundus (UWF) images. METHODS: A total of 26,409 UWF images from 14,994 subjects were used to develop and evaluate the deep learning system. The Zhongshan Ophthalmic Center (ZOC) dataset was selected to compare the performance of the system to that of retina specialists in RED detection. The saliency map visualization technique was used to understand which areas in the UWF image had the most influence on our deep learning system when detecting RED. RESULTS: The system for RED detection achieved areas under the receiver operating characteristic curve of 0.994 (95% confidence interval [CI]: 0.991-0.996), 0.972 (95% CI: 0.957-0.984), and 0.988 (95% CI: 0.983-0.992) in three independent datasets. The performance of the system in the ZOC dataset was comparable to that of an experienced retina specialist. Regions of RED were highlighted by saliency maps in UWF images. CONCLUSIONS: Our deep learning system is reliable in the automated detection of RED in UWF images. As a screening tool, our system may promote the early diagnosis and management of RED-related fundus diseases.

9.
Br J Ophthalmol ; 2021 Aug 04.
Artigo em Inglês | MEDLINE | ID: mdl-34348922

RESUMO

AIMS: To develop a deep learning (DL) model for automatic classification of macular hole (MH) aetiology (idiopathic or secondary), and a multimodal deep fusion network (MDFN) model for reliable prediction of MH status (closed or open) at 1 month after vitrectomy and internal limiting membrane peeling (VILMP). METHODS: In this multicentre retrospective cohort study, a total of 330 MH eyes with 1082 optical coherence tomography (OCT) images and 3300 clinical data enrolled from four ophthalmic centres were used to train, validate and externally test the DL and MDFN models. 266 eyes from three centres were randomly split by eye-level into a training set (80%) and a validation set (20%). In the external testing dataset, 64 eyes were included from the remaining centre. All eyes underwent macular OCT scanning at baseline and 1 month after VILMP. The area under the receiver operated characteristic curve (AUC), accuracy, specificity and sensitivity were used to evaluate the performance of the models. RESULTS: In the external testing set, the AUC, accuracy, specificity and sensitivity of the MH aetiology classification model were 0.965, 0.950, 0.870 and 0.938, respectively; the AUC, accuracy, specificity and sensitivity of the postoperative MH status prediction model were 0.904, 0.825, 0.977 and 0.766, respectively; the AUC, accuracy, specificity and sensitivity of the postoperative idiopathic MH status prediction model were 0.947, 0.875, 0.815 and 0.979, respectively. CONCLUSION: Our DL-based models can accurately classify the MH aetiology and predict the MH status after VILMP. These models would help ophthalmologists in diagnosis and surgical planning of MH.

10.
Asia Pac J Ophthalmol (Phila) ; 10(3): 234-243, 2021 Jul 01.
Artigo em Inglês | MEDLINE | ID: mdl-34224468

RESUMO

ABSTRACT: Teleophthalmology, a subfield of telemedicine, has recently been widely applied in ophthalmic disease management, accelerated by ubiquitous connectivity via mobile computing and communication applications. Teleophthalmology has strengths in overcoming geographic barriers and broadening access to medical resources, as a supplement to face-to-face clinical settings. Eyes, especially the anterior segment, are one of the most researched superficial parts of the human body. Therefore, ophthalmic images, easily captured by portable devices, have been widely applied in teleophthalmology, boosted by advancements in software and hardware in recent years. This review aims to revise current teleophthalmology applications in the anterior segment and other diseases from a temporal and spatial perspective, and summarize common scenarios in teleophthalmology, including screening, diagnosis, treatment, monitoring, postoperative follow-up, and tele-education of patients and clinical practitioners. Further, challenges in the current application of teleophthalmology and the future development of teleophthalmology are discussed.


Assuntos
Oftalmopatias , Oftalmologia , Telemedicina , Olho , Oftalmopatias/diagnóstico , Oftalmopatias/terapia , Humanos , Programas de Rastreamento
11.
Front Neurosci ; 15: 648863, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34194292

RESUMO

Visual neuroadaptation is believed to play an important role in determining the final visual outcomes following intraocular lens (IOL) implantation. To investigate visual neuroadaptation in patients with age-related cataracts (ARCs) after phacoemulsification with multifocal and monofocal IOL implantation, we conducted a prospective, controlled clinical trial in Zhongshan Ophthalmology Center. This study included 22 patients with bilateral ARCs: 11 patients underwent phacoemulsification and multifocal IOL (Mu-IOL) implantation, and 11 patients underwent phacoemulsification and monofocal IOL (Mo-IOL) implantation. Visual disturbances (glare and halos), visual function (including visual acuity, retinal straylight, contrast sensitivity, and visual evoked potentials) and visual cortical function (fractional amplitude of low-frequency fluctuations, fALFF) in Bowman's areas 17-19 as the region of interest were assessed before and after surgeries. The results showed that the fALFF values of the visual cortex in the Mu-IOL group decreased at 1 week postoperatively and recovered to baseline at 3 months and then improved at 6 months, compared with preoperative levels (at a whole-brain threshold of P < 0.05, AlphaSim-corrected, voxels > 228, repeated measures analysis of variance). Significantly increased fALFF values in the visual cortex were detected 1 week after surgery in the Mo-IOL group and decreased to baseline at 3 and 6 months. The fALFF of the lingual gyrus was negatively correlated with visual disturbances (P < 0.05). To conclude, early postoperative visual neuroadaptation was detected in the Mu-IOL group by resting-state fMRI analysis. The different changing trends of postoperative fALFF values in the two groups indicated distinct neuroadaptations patterns after Mu-IOL and Mo-IOL implantation.

12.
Ann Transl Med ; 9(9): 745, 2021 May.
Artigo em Inglês | MEDLINE | ID: mdl-34268358

RESUMO

Background: To assess associations of high academic performance with ametropia prevalence and myopia development in Chinese schoolchildren. Methods: This multicohort observational study was performed in Guangdong, China. We first performed a cross-sectional cohort analysis of students in grades 1 to 9 from Yangjiang to evaluate the relationship between academic performance and refractive status on a yearly basis. We also performed longitudinal analyses of students in Shenzhen to evaluate the trend of academic performance with refractive changes over a period of 33 months. All refractive statuses were measured using noncycloplegic autorefractors. Results: A total of 32,360 children with or without myopia were recruited in this study (mean age 10.08 years, 18,360 males and 14,000 females). Cross-sectional cohort analyses in Yangjiang showed that the prevalence of hyperopia was associated with lower academic scores in grade one, the year students entered primary school (ß=-0.04, P=0.01), whereas the prevalence of myopia was associated with higher academic scores in grade six and grade eight, the years in which students were about to take entrance examinations for junior high school or senior high school (ß=0.020, P=0.038; ß=0.041, P=0.002). Longitudinal analysis showed that in Shenzhen, faster myopia development was associated with better scores in all grades even after adjustments for BMI, outdoor activity time, screen time, reading time, and parental myopia (grade two at baseline: ß=0.026, P<0.001; grade three at baseline: ß=0.036, P=0.001; grade four at baseline: ß=0.014, P<0.001; grade five at baseline: ß=0.039, P<0.001; grade six at baseline: ß=0.04, P<0.001). Conclusions: Refractive errors correlated significantly with academic performance among schoolchildren in China. Children with high academic performance were more likely to have faster myopia development.

13.
Lancet Digit Health ; 3(8): e486-e495, 2021 08.
Artigo em Inglês | MEDLINE | ID: mdl-34325853

RESUMO

BACKGROUND: Medical artificial intelligence (AI) has entered the clinical implementation phase, although real-world performance of deep-learning systems (DLSs) for screening fundus disease remains unsatisfactory. Our study aimed to train a clinically applicable DLS for fundus diseases using data derived from the real world, and externally test the model using fundus photographs collected prospectively from the settings in which the model would most likely be adopted. METHODS: In this national real-world evidence study, we trained a DLS, the Comprehensive AI Retinal Expert (CARE) system, to identify the 14 most common retinal abnormalities using 207 228 colour fundus photographs derived from 16 clinical settings with different disease distributions. CARE was internally validated using 21 867 photographs and externally tested using 18 136 photographs prospectively collected from 35 real-world settings across China where CARE might be adopted, including eight tertiary hospitals, six community hospitals, and 21 physical examination centres. The performance of CARE was further compared with that of 16 ophthalmologists and tested using datasets with non-Chinese ethnicities and previously unused camera types. This study was registered with ClinicalTrials.gov, NCT04213430, and is currently closed. FINDINGS: The area under the receiver operating characteristic curve (AUC) in the internal validation set was 0·955 (SD 0·046). AUC values in the external test set were 0·965 (0·035) in tertiary hospitals, 0·983 (0·031) in community hospitals, and 0·953 (0·042) in physical examination centres. The performance of CARE was similar to that of ophthalmologists. Large variations in sensitivity were observed among the ophthalmologists in different regions and with varying experience. The system retained strong identification performance when tested using the non-Chinese dataset (AUC 0·960, 95% CI 0·957-0·964 in referable diabetic retinopathy). INTERPRETATION: Our DLS (CARE) showed satisfactory performance for screening multiple retinal abnormalities in real-world settings using prospectively collected fundus photographs, and so could allow the system to be implemented and adopted for clinical care. FUNDING: This study was funded by the National Key R&D Programme of China, the Science and Technology Planning Projects of Guangdong Province, the National Natural Science Foundation of China, the Natural Science Foundation of Guangdong Province, and the Fundamental Research Funds for the Central Universities. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.


Assuntos
Aprendizado Profundo , Sistemas Especialistas , Processamento de Imagem Assistida por Computador/métodos , Programas de Rastreamento/métodos , Modelos Biológicos , Retina , Doenças Retinianas/diagnóstico , Área Sob a Curva , Inteligência Artificial , Tecnologia Biomédica , China , Retinopatia Diabética/diagnóstico , Fundo de Olho , Humanos , Oftalmologistas , Fotografação , Curva ROC
14.
J Med Microbiol ; 70(7)2021 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-34296983

RESUMO

Introduction. Shigella flexneri is an intracellular bacterial pathogen that utilizes a type III secretion apparatus to inject effector proteins into host cells.Hypothesis/Gap Statement. The T3SS effector IpaH4.5 is important for the virulence of Shigella.Aim. This study aimed to elucidate the molecular mechanism and host target of the IpaH4.5 as well as its roles in S. flexneri infection.Methodology. The GAP assay was used to identify substrate Rab GTPases of IpaH4.5. A coimmunoprecipitation assay was applied to identify the interaction of Rab GTPases with IpaH4.5. A confocal microscopy analysis was used to assess the effects of IpaH4.5 on mannose 6-phosphate receptor (MPR) trafficking. To identify the effects of IpaH4.5 GAP activity on the activity of lysosomal cathepsin B, the Magic Red-RR assay was used. Finally, the intracellular persistence assay was used to identify IpaH4.5 GAP activity in S. flexneri intracellular growth.Results. We found that the effector IpaH4.5 disrupts MPR trafficking and lysosomal function, thereby counteracting host lysosomal degradation. IpaH4.5 harbours TBC-like dual-finger motifs and exhibits potent RabGAP activities towards Rab31. IpaH4.5 disrupts the transport of the cation-dependent mannose 6-phosphate receptor (CD-MPR) from the Golgi to the endosome by targeting Rab31, thereby attenuating lysosomal function. As a result, the intracellular persistence of S. flexneri requires IpaH4.5 TBC-like GAP activity to mediate bacterial escape from host lysosome-mediated elimination.Conclusion. We identified an unknown function of IpaH4.5 and its potential role in S. flexneri infection.


Assuntos
Antígenos de Bactérias/metabolismo , Proteínas de Bactérias/metabolismo , Lisossomos/metabolismo , Shigella flexneri/patogenicidade , Proteínas rab de Ligação ao GTP/metabolismo , Antígenos de Bactérias/química , Antígenos de Bactérias/genética , Proteínas de Bactérias/química , Proteínas de Bactérias/genética , Domínio Catalítico , Catepsina B/metabolismo , Endossomos/metabolismo , Proteínas Ativadoras de GTPase/química , Proteínas Ativadoras de GTPase/genética , Proteínas Ativadoras de GTPase/metabolismo , Complexo de Golgi/metabolismo , Células HEK293 , Células HeLa , Humanos , Transporte Proteico , Receptor IGF Tipo 2/metabolismo , Shigella flexneri/metabolismo , Proteínas rab de Ligação ao GTP/genética
15.
Front Bioeng Biotechnol ; 9: 662749, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34295877

RESUMO

Aim: After neoadjuvant chemotherapy (NACT), tumor shrinkage pattern is a more reasonable outcome to decide a possible breast-conserving surgery (BCS) than pathological complete response (pCR). The aim of this article was to establish a machine learning model combining radiomics features from multiparametric MRI (mpMRI) and clinicopathologic characteristics, for early prediction of tumor shrinkage pattern prior to NACT in breast cancer. Materials and Methods: This study included 199 patients with breast cancer who successfully completed NACT and underwent following breast surgery. For each patient, 4,198 radiomics features were extracted from the segmented 3D regions of interest (ROI) in mpMRI sequences such as T1-weighted dynamic contrast-enhanced imaging (T1-DCE), fat-suppressed T2-weighted imaging (T2WI), and apparent diffusion coefficient (ADC) map. The feature selection and supervised machine learning algorithms were used to identify the predictors correlated with tumor shrinkage pattern as follows: (1) reducing the feature dimension by using ANOVA and the least absolute shrinkage and selection operator (LASSO) with 10-fold cross-validation, (2) splitting the dataset into a training dataset and testing dataset, and constructing prediction models using 12 classification algorithms, and (3) assessing the model performance through an area under the curve (AUC), accuracy, sensitivity, and specificity. We also compared the most discriminative model in different molecular subtypes of breast cancer. Results: The Multilayer Perception (MLP) neural network achieved higher AUC and accuracy than other classifiers. The radiomics model achieved a mean AUC of 0.975 (accuracy = 0.912) on the training dataset and 0.900 (accuracy = 0.828) on the testing dataset with 30-round 6-fold cross-validation. When incorporating clinicopathologic characteristics, the mean AUC was 0.985 (accuracy = 0.930) on the training dataset and 0.939 (accuracy = 0.870) on the testing dataset. The model further achieved good AUC on the testing dataset with 30-round 5-fold cross-validation in three molecular subtypes of breast cancer as following: (1) HR+/HER2-: 0.901 (accuracy = 0.816), (2) HER2+: 0.940 (accuracy = 0.865), and (3) TN: 0.837 (accuracy = 0.811). Conclusions: It is feasible that our machine learning model combining radiomics features and clinical characteristics could provide a potential tool to predict tumor shrinkage patterns prior to NACT. Our prediction model will be valuable in guiding NACT and surgical treatment in breast cancer.

16.
Front Med (Lausanne) ; 8: 707242, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34307431

RESUMO

Post-keratoplasty infectious keratitis (PKIK) represents a unique clinical entity that often poses significant diagnostic and therapeutic challenges. It carries a high risk of serious complications such as graft rejection and failure, and less commonly endophthalmitis. Topical corticosteroids are often required to reduce the risk of graft rejection but their use in PKIK may act as a double-edged sword, particularly in fungal infection. The increased uptake in lamellar keratoplasty in the recent years has also led to complications such as graft-host interface infectious keratitis (IIK), which is particularly difficult to manage. The reported incidence of PKIK differs considerably across different countries, with a higher incidence observed in developing countries (9.2-11.9%) than developed countries (0.02-7.9%). Common risk factors for PKIK include the use of topical corticosteroids, suture-related problems, ocular surface diseases and previous corneal infection. PKIK after penetrating keratoplasty or (deep) anterior lamellar keratoplasty is most commonly caused by ocular surface commensals, particularly Gramme-positive bacteria, whereas PKIK after endothelial keratoplasty is usually caused by Candida spp. Empirical broad-spectrum antimicrobial treatment is the mainstay of treatment for both PKIK, though surgical interventions are required in medically refractory cases (during the acute phase) and those affected by visually significant scarring (during the late phase). In this paper, we aim to provide a comprehensive overview on PKIK, encompassing the epidemiology, risk factors, causes, management and outcomes, and to propose a treatment algorithm for systematically managing this challenging condition.

17.
Ann Transl Med ; 9(7): 550, 2021 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-33987248

RESUMO

Background: Lens opacity seriously affects the visual development of infants. Slit-illumination images play an irreplaceable role in lens opacity detection; however, these images exhibited varied phenotypes with severe heterogeneity and complexity, particularly among pediatric cataracts. Therefore, it is urgently needed to explore an effective computer-aided method to automatically diagnose heterogeneous lens opacity and to provide appropriate treatment recommendations in a timely manner. Methods: We integrated three different deep learning networks and a cost-sensitive method into an ensemble learning architecture, and then proposed an effective model called CCNN-Ensemble [ensemble of cost-sensitive convolutional neural networks (CNNs)] for automatic lens opacity detection. A total of 470 slit-illumination images of pediatric cataracts were used for training and comparison between the CCNN-Ensemble model and conventional methods. Finally, we used two external datasets (132 independent test images and 79 Internet-based images) to further evaluate the model's generalizability and effectiveness. Results: Experimental results and comparative analyses demonstrated that the proposed method was superior to conventional approaches and provided clinically meaningful performance in terms of three grading indices of lens opacity: area (specificity and sensitivity; 92.00% and 92.31%), density (93.85% and 91.43%) and opacity location (95.25% and 89.29%). Furthermore, the comparable performance on the independent testing dataset and the internet-based images verified the effectiveness and generalizability of the model. Finally, we developed and implemented a website-based automatic diagnosis software for pediatric cataract grading diagnosis in ophthalmology clinics. Conclusions: The CCNN-Ensemble method demonstrates higher specificity and sensitivity than conventional methods on multi-source datasets. This study provides a practical strategy for heterogeneous lens opacity diagnosis and has the potential to be applied to the analysis of other medical images.

18.
Ann Transl Med ; 9(7): 554, 2021 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-33987252

RESUMO

Background: Myopia is a complex disease caused by a combination of multiple pathogenic factors. Prevalence trends and developmental patterns of myopia exhibit substantial variability that cannot be clearly assessed using limited sample sizes. This study aims to determine the myopia prevalence over the past 60 years and trace the myopia development in a school-aged population using medical big data. Methods: The refraction data from electronic medical records in eight hospitals in South China were collected from January 2005 to October 2018; including patients' year of birth, refraction status, and age at the exam. All optometry tests were performed in accordance with standard procedures by qualified senior optometrists. The cross-sectional datasets (individuals with a single examination) and longitudinal datasets (individuals with multiple examinations) were analyzed respectively. SAS statistical software was used to extract and statistically analyse all target data and to identify prevalence trends and developmental patterns related to myopia. Results: In total, 1,112,054 cross-sectional individual refraction records and 774,645 longitudinal records of 273,006 individuals were collected. The myopia prevalence significantly increased among individuals who were born after the 1960s and showed a steep rise until reaching a peak of 80% at the 1980s. Regarding developmental patterns, the cross-sectional data demonstrated that the myopia prevalence increased dramatically from 23.13% to 82.83% aging from 5 to 11, and the prevalence stabilized at the age of 20. The longitudinal data confirmed the results that the age of myopic onset was 7.47±1.67 years, the age of myopia stabilized at 17.14±2.61 years, and the degree of myopia stabilized at -4.35±3.81 D. Conclusions: The medical big data used in this study demonstrated prevalence trends of myopia over the past 60 years and revealed developmental patterns in the onset, progression and stability of myopia in China.

19.
Front Med (Lausanne) ; 8: 664023, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34026791

RESUMO

Infantile cataract is the main cause of infant blindness worldwide. Although previous studies developed artificial intelligence (AI) diagnostic systems for detecting infantile cataracts in a single center, its generalizability is not ideal because of the complicated noises and heterogeneity of multicenter slit-lamp images, which impedes the application of these AI systems in real-world clinics. In this study, we developed two lens partition strategies (LPSs) based on deep learning Faster R-CNN and Hough transform for improving the generalizability of infantile cataracts detection. A total of 1,643 multicenter slit-lamp images collected from five ophthalmic clinics were used to evaluate the performance of LPSs. The generalizability of Faster R-CNN for screening and grading was explored by sequentially adding multicenter images to the training dataset. For the normal and abnormal lenses partition, the Faster R-CNN achieved the average intersection over union of 0.9419 and 0.9107, respectively, and their average precisions are both > 95%. Compared with the Hough transform, the accuracy, specificity, and sensitivity of Faster R-CNN for opacity area grading were improved by 5.31, 8.09, and 3.29%, respectively. Similar improvements were presented on the other grading of opacity density and location. The minimal training sample size required by Faster R-CNN is determined on multicenter slit-lamp images. Furthermore, the Faster R-CNN achieved real-time lens partition with only 0.25 s for a single image, whereas the Hough transform needs 34.46 s. Finally, using Grad-Cam and t-SNE techniques, the most relevant lesion regions were highlighted in heatmaps, and the high-level features were discriminated. This study provides an effective LPS for improving the generalizability of infantile cataracts detection. This system has the potential to be applied to multicenter slit-lamp images.

20.
Ann Transl Med ; 9(5): 374, 2021 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-33842595

RESUMO

Background: Strabismus affects approximately 0.8-6.8% of the world's population and can lead to abnormal visual function. However, Strabismus screening and measurement are laborious and require professional training. This study aimed to develop an artificial intelligence (AI) platform based on corneal light-reflection photos for the diagnosis of strabismus and to provide preoperative advice. Methods: An AI platform consisting of three deep learning (DL) systems for strabismus diagnosis, angle evaluation, and operation plannings based on corneal light-reflection photos was trained and retrospectively validated using a retrospective development data set obtained between Jan 1, 2014, and Dec 31, 2018. Corneal light-reflection photos were collected to train the DL systems for strabismus screening and deviation evaluations in the horizontal strabismus while concatenated images (each composed of two photos representing different gaze states) were procured to train the DL system for operative advice regarding exotropia. The AI platform was further prospectively validated using a prospective development data set captured between Sep 1, 2019, and Jun 10, 2020. Results: In total, 5,797 and 571 photos were included in the retrospective and prospectively development data sets, respectively. In the retrospective test sets, the screening system detected strabismus with a sensitivity of 99.1% [95% confidence interval (95% CI), 98.1-99.7%], a specificity of 98.3% (95% CI, 94.6-99.5%), and an AUC of 0.998 (95% CI, 0.993-1.000, P<0.001). Compared to the angle measured by the perimeter arc, the deviation evaluation system achieved a level of accuracy of ±6.6º (95% LoA) with a small bias of 1.0º. Compared to the real design, the operation advice system provided advice regarding the target angle within ±5.5º (95% LoA). Regarding strabismus in the prospective test set, the AUC was 0.980. The platform achieved a level of accuracy of ±7.0º (95% LoA) in the deviation evaluation and ±6.1º (95% LoA) in the target angle suggestion. Conclusions: The AI platform based on corneal light-reflection photos can provide reliable references for strabismus diagnosis, angle evaluation, and surgical plannings.

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