Multicenter Validation of Deep Learning Algorithm ROP.AI for the Automated Diagnosis of Plus Disease in ROP.

Purpose: Retinopathy of prematurity (ROP) is a sight-threatening vasoproliferative retinal disease affecting premature infants. The detection of plus disease, a severe form of ROP requiring treatment, remains challenging owing to subjectivity, frequency, and time intensity of retinal examinations. Recent artificial intelligence (AI) algorithms developed to detect plus disease aims to alleviate these challenges; however, they have not been tested against a diverse neonatal population. Our study aims to validate ROP.AI, an AI algorithm developed from a single cohort, against a multicenter Australian cohort to determine its performance in detecting plus disease. Methods: Retinal images captured during routine ROP screening from May 2021 to February 2022 across five major tertiary centers throughout Australia were collected and uploaded to ROP.AI. AI diagnostic output was compared with one of five ROP experts. Sensitivity, specificity, negative predictive value, and area under the receiver operator curve were determined. Results: We collected 8052 images. The area under the receiver operator curve for the diagnosis of plus disease was 0.75. ROP.AI achieved 84% sensitivity, 43% specificity, and 96% negative predictive value for the detection of plus disease after operating point optimization. Conclusions: ROP.AI was able to detect plus disease in an external, multicenter cohort despite being trained from a single center. Algorithm performance was demonstrated without preprocessing or augmentation, simulating real-world clinical applicability. Further training may improve generalizability for clinical implementation. Translational Relevance: These results demonstrate ROP.AI's potential as a screening tool for the detection of plus disease in future clinical practice and provides a solution to overcome current diagnostic challenges.

The first year of the Covid-19 pandemic through the lens of r/Coronavirus subreddit: an exploratory study.

Data: This study looks at the content on Reddit's COVID-19 community, r/Coronavirus, to capture and understand the main themes and discussions around the global pandemic, and their evolution over the first year of the pandemic. It studies 356,690 submissions (posts) and 9,413,331 comments associated with the submissions, corresponding to the period of 20th January 2020 and 31st January 2021. Methodology: On each of these datasets we carried out analysis based on lexical sentiment and topics generated from unsupervised topic modelling. The study found that negative sentiments show higher ratio in submissions while negative sentiments were of the same ratio as positive ones in the comments. Terms associated more positively or negatively were identified. Upon assessment of the upvotes and downvotes, this study also uncovered contentious topics, particularly "fake" or misleading news. Results: Through topic modelling, 9 distinct topics were identified from submissions while 20 were identified from comments. Overall, this study provides a clear overview on the dominating topics and popular sentiments pertaining the pandemic during the first year. Conclusion: Our methodology provides an invaluable tool for governments and health decision makers and authorities to obtain a deeper understanding of the dominant public concerns and attitudes, which is vital for understanding, designing and implementing interventions for a global pandemic.

Interventions recommended for myopia prevention and control among children and adolescents in China: a systematic review.

In 2018, a consortium of government bodies in China led by the Ministry of Education released the Comprehensive Plan to Prevent Nearsightedness among Children and Teenagers (CPPNCT), aiming to reduce the incidence of myopia and control myopic progression in China. Recommendations span from home-based to school-based interventions, including time outdoors, physical activity, light exposure, near-work activity, screen time, Chinese eye exercises, diet and sleep. To date, the levels of evidence for this suite of interventions have not been thoroughly investigated. This review has summarised the evidence of the interventions recommended by the CPPNCT in myopia prevention and control. Thus, the following statements are supposed by the evidence: (1) Increasing time outdoors and reducing near-work time are effective in lowering incident myopia in school-aged children. (2) All interventions have a limited effect on myopia progression. Ongoing research may lead to a better understanding of the underlying mechanisms of myopia development, the interaction of different interventions and recommendations, confounding variables and their true effect on myopia prevention, and the identification of those most likely to respond to specific interventions. This field may also benefit from longer-term studies of the various interventions or strategies covered within this review article, to better understand the persistence of treatment effects over time and explore more novel approaches to myopia control.

Retinal age gap as a predictive biomarker for mortality risk.

AIM: To develop a deep learning (DL) model that predicts age from fundus images (retinal age) and to investigate the association between retinal age gap (retinal age predicted by DL model minus chronological age) and mortality risk. METHODS: A total of 80 169 fundus images taken from 46 969 participants in the UK Biobank with reasonable quality were included in this study. Of these, 19 200 fundus images from 11 052 participants without prior medical history at the baseline examination were used to train and validate the DL model for age prediction using fivefold cross-validation. A total of 35 913 of the remaining 35 917 participants had available mortality data and were used to investigate the association between retinal age gap and mortality. RESULTS: The DL model achieved a strong correlation of 0.81 (p<0·001) between retinal age and chronological age, and an overall mean absolute error of 3.55 years. Cox regression models showed that each 1 year increase in the retinal age gap was associated with a 2% increase in risk of all-cause mortality (hazard ratio (HR)=1.02, 95% CI 1.00 to 1.03, p=0.020) and a 3% increase in risk of cause-specific mortality attributable to non-cardiovascular and non-cancer disease (HR=1.03, 95% CI 1.00 to 1.05, p=0.041) after multivariable adjustments. No significant association was identified between retinal age gap and cardiovascular- or cancer-related mortality. CONCLUSIONS: Our findings indicate that retinal age gap might be a potential biomarker of ageing that is closely related to risk of mortality, implying the potential of retinal image as a screening tool for risk stratification and delivery of tailored interventions.

Self-reported cataract surgery and 10-year all-cause and cause-specific mortality: findings from the National Health and Nutrition Examination Survey.

PURPOSE: To investigate the association of self-reported cataract surgery with all-cause and cause-specific mortality using a large-scale population-based sample. METHODS: Data from the 1999-2008 cycles of the National Health and Nutrition Examination Survey were used. A self-reported history of cataract surgery was considered a surrogate for the presence of clinically significant cataract surgery. Mortality data were ascertained from National Death Index records. Hazard ratios (HRs) and 95% confidence intervals (CIs) for survival were estimated using Cox proportional hazards regression models. RESULTS: A total of 14 918 participants were included in the analysis. During a median follow-up of 10.8 (Interquartile range, IQR, 8.25-13.7) years, 3966 (19.1%) participants died. Participants with self-reported cataract surgery were more likely to die from all causes and specific causes (vascular disease, cancer, accident, Alzheimer's disease, respiratory disease, renal disease and others) compared with those without (all Ps <0.05). The association between self-reported cataract surgery and all-cause mortality remained significant after multiple adjustments (HR=1.13; 95% CI 1.01 to 1.26). For cause-specific mortality, multivariable Cox models showed that self-reported cataract surgery predicted a 36% higher risk of vascular-related mortality (HR=1.36; 95% CI 1.01 to 1.82). The association with other specific causes of mortality did not reach statistical significance after multiple adjustments. CONCLUSIONS: This study found significant associations of self-reported cataract surgery with all-cause and vascular mortalities. Our findings provide potential insights into the pathogenic pathways underlying cataract.

Retinopathy of prematurity screening: A narrative review of current programs, teleophthalmology, and diagnostic support systems.

PURPOSE: Neonatal care in middle-income countries has improved over the last decade, leading to a "third epidemic" of retinopathy of prematurity (ROP). Without concomitant improvements in ROP screening infrastructure, reduction of ROP-associated visual loss remains a challenge worldwide. The emergence of teleophthalmology screening programs and artificial intelligence (AI) technologies represents promising methods to address this growing unmet demand in ROP screening. An improved understanding of current ROP screening programs may inform the adoption of these novel technologies in ROP care. METHODS: A critical narrative review of the literature was carried out. Publications that were representative of established or emerging ROP screening programs in high-, middle-, and low-income countries were selected for review. Screening programs were reviewed for inclusion criteria, screening frequency and duration, modality, and published sensitivity and specificity. RESULTS: Screening inclusion criteria, including age and birth weight cutoffs, showed significant heterogeneity globally. Countries of similar income tend to have similar criteria. Three primary screening modalities including binocular indirect ophthalmoscopy (BIO), wide-field digital retinal imaging (WFDRI), and teleophthalmology were identified and reviewed. BIO has documented limitations in reduced interoperator agreement, scalability, and geographical access barriers, which are mitigated in part by WFDRI. Teleophthalmology screening may address limitations in ROP screening workforce distribution and training. Opportunities for AI technologies were identified in the context of these limitations, including interoperator reliability and possibilities for point-of-care diagnosis. CONCLUSION: Limitations in the current ROP screening include scalability, geographical access, and high screening burden with low treatment yield. These may be addressable through increased adoption of teleophthalmology and AI technologies. As the global incidence of ROP continues to increase, implementation of these novel modalities requires greater consideration.

Geographical Inequality on Cataract Surgery Uptake in 200,000 Australians: Findings from the "45 and Up Study".

Using a geographical information system (GIS), we investigated the spatiotemporal evolution of a cataract surgery service and its association with socioeconomic factors and private insurance, based on 10-year real-world medical claim data in an Australian population. The data collected cover a decade (2007-2016) from the "45 and Up Study". A total of 234,201 participants within the cataract surgery service were grouped into 88 Statistical Area Level 3 (SA3s) according to their residential postcodes in New South Wales Australia. We analyzed the spatiotemporal variations and geographical distribution inequality in cataract surgery incidence and its respect to socioeconomic status (SES) and private health insurance coverage by Spearman correlation analysis and Moran's I test. Then these variations were intuitive displayed by six-quartile maps and a local indicator of spatial association (LISA) maps based on GIS. The average cumulative age-gender-standardized of the incidence of cataract surgery (ICS) was 8.85% (95% CI, 5.33-15.6). Spatial variation was significant (univariate Moran's I = 0.45, P = 0.001) with incidence gradually decreasing from the coastal regions to the north-western inland regions, suggesting inequality in the cataract surgery service across the state of New South Wales. Notably, clustering of the low incidence areas had gradually disappeared over the decade, suggesting that the cataract surgery service has improved over time. Low scores on the "index of socioeconomic disadvantages" (IRSD) and high private health insurance coverage were significantly associated with a higher incidence of cataract surgery (bivariate Moran's I = -0.13 and 0.23, P < 0.01; Spearman correlation r = 0.25 and -0.25, P = 0.02), which is displayed on the map visually and obviously. Spatiotemporal variations in the incidence of cataract surgery are significant, but the low incidence area had gradually disappeared over time. High socioeconomic status and private insurance contribute to a higher incidence of cataract surgery in Australia.

Association of Retinal Age Gap With Arterial Stiffness and Incident Cardiovascular Disease.

BACKGROUND: Retinal parameters could reflect systemic vascular changes. With the advances of deep learning technology, we have recently developed an algorithm to predict retinal age based on fundus images, which could be a novel biomarker for aging and mortality. Therefore, we aim to investigate associations of retinal age gap with arterial stiffness index and incident cardiovascular disease (CVD). METHODS: A deep learning model was trained based on 19 200 fundus images of 11 052 participants without any medical history at baseline to predict the retinal age. Retinal age gap (retinal age predicted minus chronological age) was generated for the remaining 35 917 participants. Regression models were used to assess the association between retinal age gap and arterial stiffness index. Cox proportional hazards regression models and restricted cubic splines were used to explore the association between retinal age gap and incident CVD. RESULTS: We found each 1-year increase in retinal age gap was associated with increased arterial stiffness index (ß=0.002 [95% CI, 0.001-0.003]; P<0.001). After a median follow-up of 5.83 years (interquartile range: 5.73-5.97), 675 (2.00%) developed CVD. In the fully adjusted model, each 1-year increase in retinal age gap was associated with a 3% increase in the risk of incident CVD (hazard ratio=1.03 [95% CI, 1.01-1.06]; P=0.014). In the restricted cubic splines analysis, the risk of incident CVD increased significantly when retinal age gap reached 1.21 (hazard ratio=1.05 [95% CI, 1.00-1.10]; P-overall <0.0001; P-nonlinear=0.0681). CONCLUSIONS: We found that retinal age gap was significantly associated with arterial stiffness index and incident CVD events, supporting the potential of this novel biomarker in identifying individuals at high risk of future CVD events.

A Deep Learning System for Fully Automated Retinal Vessel Measurement in High Throughput Image Analysis.

Motivation: Retinal microvasculature is a unique window for predicting and monitoring major cardiovascular diseases, but high throughput tools based on deep learning for in-detail retinal vessel analysis are lacking. As such, we aim to develop and validate an artificial intelligence system (Retina-based Microvascular Health Assessment System, RMHAS) for fully automated vessel segmentation and quantification of the retinal microvasculature. Results: RMHAS achieved good segmentation accuracy across datasets with diverse eye conditions and image resolutions, having AUCs of 0.91, 0.88, 0.95, 0.93, 0.97, 0.95, 0.94 for artery segmentation and 0.92, 0.90, 0.96, 0.95, 0.97, 0.95, 0.96 for vein segmentation on the AV-WIDE, AVRDB, HRF, IOSTAR, LES-AV, RITE, and our internal datasets. Agreement and repeatability analysis supported the robustness of the algorithm. For vessel analysis in quantity, less than 2 s were needed to complete all required analysis.

Screening Referable Diabetic Retinopathy Using a Semi-automated Deep Learning Algorithm Assisted Approach.

Purpose: To assess the accuracy and efficacy of a semi-automated deep learning algorithm (DLA) assisted approach to detect vision-threatening diabetic retinopathy (DR). Methods: We developed a two-step semi-automated DLA-assisted approach to grade fundus photographs for vision-threatening referable DR. Study images were obtained from the Lingtou Cohort Study, and captured at participant enrollment in 2009-2010 ("baseline images") and annual follow-up between 2011 and 2017. To begin, a validated DLA automatically graded baseline images for referable DR and classified them as positive, negative, or ungradable. Following, each positive image, all other available images from patients who had a positive image, and a 5% random sample of all negative images were selected and regraded by trained human graders. A reference standard diagnosis was assigned once all graders achieved consistent grading outcomes or with a senior ophthalmologist's final diagnosis. The semi-automated DLA assisted approach combined initial DLA screening and subsequent human grading for images identified as high-risk. This approach was further validated within the follow-up image datasets and its time and economic costs evaluated against fully human grading. Results: For evaluation of baseline images, a total of 33,115 images were included and automatically graded by the DLA. 2,604 images (480 positive results, 624 available other images from participants with a positive result, and 1500 random negative samples) were selected and regraded by graders. The DLA achieved an area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy of 0.953, 0.970, 0.879, and 88.6%, respectively. In further validation within the follow-up image datasets, a total of 88,363 images were graded using this semi-automated approach and human grading was performed on 8975 selected images. The DLA achieved an AUC, sensitivity, and specificity of 0.914, 0.852, 0.853, respectively. Compared against fully human grading, the semi-automated DLA-assisted approach achieved an estimated 75.6% time and 90.1% economic cost saving. Conclusions: The DLA described in this study was able to achieve high accuracy, sensitivity, and specificity in grading fundus images for referable DR. Validated against long-term follow-up datasets, a semi-automated DLA-assisted approach was able to accurately identify suspect cases, and minimize misdiagnosis whilst balancing safety, time, and economic cost.

Association of visual impairment with risk for future Parkinson's disease.

BACKGROUND: Although visual dysfunction is one of the most common non-motor symptoms among patients with Parkinson's disease (PD), it is not known whether visual impairment (VI) predates the onset of clinical PD. Therefore, we aim to examine the association of VI with the future development of PD in the UK Biobank Study. METHODS: The UK Biobank Study is one of the largest cohort studies of health, enrolling over 500,000 participants aged 40-69 years between 2006 and 2010 across the UK. VI was defined as a habitual distance visual acuity (VA) worse than 0·3 logarithm of the minimum angle of resolution (LogMAR) in the better-seeing eye. Incident cases of PD were determined by self report data, hospital admission records or death records, whichever came first. Multivariable Cox proportional hazard regression models were used to investigate the association between VI and the risk of incident PD. FINDINGS: A total of 117,050 participants were free of PD at the baseline assessment. During the median observation period of 5·96 (IQR: 5·77-6·23) years, PD occurred in 222 (0·19%) participants. Visually impaired participants were at a higher risk of developing PD than non-VI participants (p < 0·001). Compared with the non-VI group, the adjusted hazard ratio was 2·28 (95% CI 1·29-4·05, p = 0·005) in the VI group. These results were consistent in the sensitivity analysis, where incident PD cases diagnosed within one year after the baseline assessment were excluded. INTERPRETATION: This cohort study found that VI was associated with an increased risk of incident PD, suggesting that VI may serve as a modifiable risk factor for prevention of future PD.

Adiposity by Differing Measures and the Risk of Cataract in the UK Biobank: The Importance of Diabetes.

Purpose: To examine the association between adiposity by differing measures and incident cataract and identify important factors contributing to the association. Methods: Our analysis included 153,139 adults from the UK Biobank, aged 40 to 70 years at baseline (2006-2010). Cataract was ascertained using hospital inpatient, and self-reported data until the early of 2021. Anthropometric measures, body fat percentage, and glycosylated hemoglobin (HbA1c) were measured at baseline. Results: During a median follow-up of 10.9 years, 15,255 cases of incident cataract were documented. HbA1c was an important contributor to the association between obesity and incident cataract. Obesity; defined by body mass index was associated with an increased risk of cataract (hazard ratio [HR], 1.21 95% confidence interval [CI], 1.16-1.26), and this association was attenuated but remained significant after additional adjustment for HbA1c (HR, 1.05; 95% CI, 1.00-1.10). Similar results were observed for obesity defined by waist circumference or waist-to-hip ratio. Obesity defined by fat percentage was associated with an increased risk of cataract before but not after adjustment for covariates. The association between obesity defined by body mass index and incident cataract was positively significant in individuals with normal HbA1c (HR, 1.07; 95% CI, 1.02-1.13), but inversely significant in those with prediabetes (HR, 0.80; 95% CI, 0.67-0.96) or diabetes (HR, 0.74; 95% CI, 0.61-0.89). Conclusions: Anthropometric measurements are more predictive of cataract than bioelectrical impedance measures. Diabetes plays an important role in the association between obesity and incident cataract.

Who needs myopia control?

Myopia has become a major visual disorder among school-aged children in East Asia due to its rising prevalence over the past few decades and will continue to be a leading health issue with an annual incidence as high as 20%-30%. Although various interventions have been proposed for myopia control, consensus in treatment strategies has yet to be fully developed. Atropine and orthokeratology stand out for their effectiveness in myopia progression control, but children with rapid progression of myopia require treatment with higher concentrations of atropine that are associated with increased rates of side effects, or with orthokeratology that carries risk of significant complication. Therefore, improved risk assessment for myopia onset and progression in children is critical in clinical decision-making. Besides traditional prediction models based on genetic effects and environmental exposures within populations, individualized prediction using machine learning and data based on age-specific refraction is promising. Although emerging treatments for myopia are promising and some have been incorporated into clinical practice, identifying populations who require and benefit from intervention remains the most important initial step for clinical practice.

Real-world artificial intelligence-based opportunistic screening for diabetic retinopathy in endocrinology and indigenous healthcare settings in Australia.

This study investigated the diagnostic performance, feasibility, and end-user experiences of an artificial intelligence (AI)-assisted diabetic retinopathy (DR) screening model in real-world Australian healthcare settings. The study consisted of two components: (1) DR screening of patients using an AI-assisted system and (2) in-depth interviews with health professionals involved in implementing screening. Participants with type 1 or type 2 diabetes mellitus attending two endocrinology outpatient and three Aboriginal Medical Services clinics between March 2018 and May 2019 were invited to a prospective observational study. A single 45-degree (macula centred), non-stereoscopic, colour retinal image was taken of each eye from participants and were instantly screened for referable DR using a custom offline automated AI system. A total of 236 participants, including 174 from endocrinology and 62 from Aboriginal Medical Services clinics, provided informed consent and 203 (86.0%) were included in the analysis. A total of 33 consenting participants (14%) were excluded from the primary analysis due to ungradable or missing images from small pupils (n = 21, 63.6%), cataract (n = 7, 21.2%), poor fixation (n = 2, 6.1%), technical issues (n = 2, 6.1%), and corneal scarring (n = 1, 3%). The area under the curve, sensitivity, and specificity of the AI system for referable DR were 0.92, 96.9% and 87.7%, respectively. There were 51 disagreements between the reference standard and index test diagnoses, including 29 which were manually graded as ungradable, 21 false positives, and one false negative. A total of 28 participants (11.9%) were referred for follow-up based on new ocular findings, among whom, 15 (53.6%) were able to be contacted and 9 (60%) adhered to referral. Of 207 participants who completed a satisfaction questionnaire, 93.7% specified they were either satisfied or extremely satisfied, and 93.2% specified they would be likely or extremely likely to use this service again. Clinical staff involved in screening most frequently noted that the AI system was easy to use, and the real-time diagnostic report was useful. Our study indicates that AI-assisted DR screening model is accurate and well-accepted by patients and clinicians in endocrinology and indigenous healthcare settings. Future deployments of AI-assisted screening models would require consideration of downstream referral pathways.

Deployment of Artificial Intelligence in Real-World Practice: Opportunity and Challenge.

Artificial intelligence has rapidly evolved from the experimental phase to the implementation phase in many image-driven clinical disciplines, including ophthalmology. A combination of the increasing availability of large datasets and computing power with revolutionary progress in deep learning has created unprecedented opportunities for major breakthrough improvements in the performance and accuracy of automated diagnoses that primarily focus on image recognition and feature detection. Such an automated disease classification would significantly improve the accessibility, efficiency, and cost-effectiveness of eye care systems where it is less dependent on human input, potentially enabling diagnosis to be cheaper, quicker, and more consistent. Although this technology will have a profound impact on clinical flow and practice patterns sooner or later, translating such a technology into clinical practice is challenging and requires similar levels of accountability and effectiveness as any new medication or medical device due to the potential problems of bias, and ethical, medical, and legal issues that might arise. The objective of this review is to summarize the opportunities and challenges of this transition and to facilitate the integration of artificial intelligence (AI) into routine clinical practice based on our best understanding and experience in this area.

Surgeon clinical practice variation and patient preferences during the informed consent discussion: a mixed-methods analysis in lumbar spine surgery.

Background: Patients with lumbar disc herniation may greatly benefit from microdiscectomy. Although spine surgeons performing microdiscectomy routinely obtain informed consent, the potential adverse events they disclose often vary. Moreover, little is known about what disclosures are deemed most valuable by patients. The aim of this mixed-methods study was to determine practice variations among spine surgeons in regard to the disclosure of potential adverse events during informed consent discussions for lumbar microdiscectomy and to determine which topics patients perceived to be valuable in the consent discussion. Methods: A survey evaluating the frequency with which spine surgeons disclose 15 potential adverse events related to lumbar microdiscectomy during informed consent discussions was distributed among Canadian Spine Society members. Additionally, semistructured interviews were conducted with preoperative patients, postoperative patients, attending spine surgeons, spine fellows and orthopedic residents. Interview transcripts were analyzed using thematic analysis with open coding. Results: Fifty-one Canadian Spine Society members completed the survey. The number of potential adverse events not routinely discussed was greater among orthopedic surgeons than among neurosurgeons (relative risk 1.83; 95% confidence interval 1.22-2.73; p = 0.003). Three preoperative patients, 7 postoperative patients, 6 attending spine surgeons, 3 spine fellows and 5 orthopedic residents participated in the semistructured interviews. The interviews identified gaps in information provided to patients, particularly on topics relating to postoperative care such as expected recovery time, activity restrictions and need for a caregiver. Conclusion: There is variation in the disclosure of potential adverse events during informed consent discussions for lumbar microdiscectomy among Canadian spine surgeons. Patients desire more information regarding their postoperative care. Further research should focus on developing guidelines to reduce practice variation and optimize the effectiveness of consent discussions.

Contexte: Les patients atteints d'une hernie discale lombaire pourraient profiter grandement d'une microdiscectomie. Bien que les chirurgiens spécialistes de la colonne vertébrale réalisant des microdiscectomies obtiennent toujours le consentement éclairé du patient, les événements indésirables potentiels présentés varient souvent. De plus, on en connaît peu sur les informations les plus importantes du point de vue des patients. L'objectif de cette étude à méthodes mixtes était de déterminer les différentes pratiques des chirurgiens en ce qui a trait à la présentation des événements indésirables potentiels pendant les discussions sur le consentement éclairé pour les microdiscectomies lombaires et de déterminer les sujets les plus importants pour les patients pendant ces discussions. Méthodes: Un sondage sur la fréquence à laquelle les chirurgiens présentent 15 événements indésirables potentiels associés à la microdiscectomie lombaire pendant les discussions sur le consentement éclairé a été distribué aux membres de la Société canadienne du rachis. De plus, des entretiens semi-dirigés ont été réalisés auprès de patients en période préopératoire, de patients en période postopératoire, de chirurgiens spécialistes de la colonne vertébrale, de fellows en chirurgie spinale et de résidents en chirurgie orthopédique. Des analyses thématiques utilisant un code ouvert ont été réalisées sur les transcriptions des entretiens. Résultats: Cinquante-et-un membres de la Société canadienne du rachis ont répondu au sondage. Le nombre d'événements indésirables potentiels non systématiquement mentionnés était plus élevé chez les chirurgiens orthopédiques que chez les neurochirurgiens (risque relatif 1,83; intervalle de confiance de 95 % 1,22­2,73; p = 0,003). Dans les entretiens semi-dirigés, on a recueilli les commentaires de 3 patients en période préopératoire, de 7 patients en période postopératoire, de 6 chirurgiens spécialistes de la colonne vertébrale, de 3 fellows en chirurgie spinale et de 5 résidents en chirurgie orthopédique. Les entretiens ont révélé des lacunes dans l'information transmise aux patients, particulièrement sur les soins postopératoires, comme le temps de récupération attendu, les restrictions quant aux activités et la nécessité d'un soignant. Conclusion: On a trouvé une variation dans la présentation des événements indésirables potentiels pendant les discussions sur le consentement éclairé pour les microdiscectomies lombaires chez les chirurgiens spécialistes de la colonne vertébrale au Canada. Les patients veulent en savoir plus sur les soins postopératoires. Des lignes directrices devraient être établies pour réduire les différences entre les pratiques et optimiser l'efficacité des discussions sur le consentement.

Deep Learning Algorithm for Automated Diagnosis of Retinopathy of Prematurity Plus Disease.

PURPOSE: This study describes the initial development of a deep learning algorithm, ROP.AI, to automatically diagnose retinopathy of prematurity (ROP) plus disease in fundal images. METHODS: ROP.AI was trained using 6974 fundal images from Australasian image databases. Each image was given a diagnosis as part of real-world routine ROP screening and classified as normal or plus disease. The algorithm was trained using 80% of the images and validated against the remaining 20% within a hold-out test set. Performance in diagnosing plus disease was evaluated against an external set of 90 images. Performance in detecting pre-plus disease was also tested. As a screening tool, the algorithm's operating point was optimized for sensitivity and negative predictive value, and its performance reevaluated. RESULTS: For plus disease diagnosis within the 20% hold-out test set, the algorithm achieved a 96.6% sensitivity, 98.0% specificity, and 97.3% ± 0.7% accuracy. Area under the receiver operating characteristic curve was 0.993. Within the independent test set, the algorithm achieved a 93.9% sensitivity, 80.7% specificity, and 95.8% negative predictive value. For detection of pre-plus and plus disease, the algorithm achieved 81.4% sensitivity, 80.7% specificity, and 80.7% negative predictive value. Following the identification of an optimized operating point, the algorithm diagnosed plus disease with a 97.0% sensitivity and 97.8% negative predictive value. CONCLUSIONS: ROP.AI is a deep learning algorithm able to automatically diagnose ROP plus disease with high sensitivity and negative predictive value. TRANSLATIONAL RELEVANCE: In the context of increasing global disease burden, future development may improve access to ROP diagnosis and care.

Cortical Bone Trajectory Screws for Fixation Across the Cervicothoracic Junction: Surgical Technique and Outcomes.

STUDY DESIGN: Clinical case series describing a novel surgical technique. OBJECTIVE: Stabilization across the cervicothoracic junction (CTJ) poses technical difficulties which make this procedure challenging. The transition from cervical lordosis to thoracic kyphosis and the orientation of the lateral masses of the cervical spine compared with the pedicles of the thoracic spine create the need to accommodate for 2 planes of alignment when placing instrumentation. A novel surgical technique for instrumentation across the cervicothoracic junction is described. METHODS: The use of cortical bone trajectory (CBT) technique for pedicle fixation in the upper thoracic spine is described in combination with cervical lateral mass or pedicle screws. The application in our first 12 patients for stabilization across the CTJ is described. Two case presentations illustrate the technique. RESULTS: All the patients had rod screw constructs without the need to skip levels, there was no requirement for transverse connectors and only 1 plane of contouring was required. CONCLUSIONS: The use of CBT technique has not been described for the upper thoracic spine. This technique avoids many technical problems associated with posterior instrumentation of the CTJ. The facility of their use in this application arises from the similar coronal plane entry points as the cervical lateral mass screws compared with the more lateral starting point of traditional thoracic pedicle screws. The technique has clinical equipoise to traditional thoracic pedicle screw insertion but with the benefits of an easier ability to perform the instrumentation and saving levels of fusion.

Validation of a freehand technique for cortical bone trajectory screws in the lumbar spine.

OBJECTIVEThe cortical bone trajectory (CBT) technique for pedicle screw placement has gained popularity among spinal surgeons. It has been shown biomechanically to provide better fixation and improved pullout strength compared to a traditional pedicle screw trajectory. The CBT technique also allows for a less invasive approach for fusion and may have lower incidence of adjacent-level disease. A limitation of the current CBT technique is a lack of readily identifiable and reproducible visual landmarks to guide freehand CBT screw placement in comparison to the well-defined identifiable landmarks for traditional pedicle screw insertion. The goal of this study was to validate a safe and intuitive freehand technique for placement of CBT screws based on optimization of virtual CBT screw placement using anatomical landmarks in the lumbar spine. The authors hypothesized that virtual identification of anatomical landmarks on 3D models of the lumbar spine generated from CT scans would translate to a safe intraoperative freehand technique.METHODSCustomized, open-source medical imaging and visualization software (3D Slicer) was used in this study to develop a workflow for virtual simulation of lumbar CBT screw insertion. First, in an ex vivo study, 20 anonymous CT image series of normal and degenerative lumbar spines and virtual screw insertion were conducted to place CBT screws bilaterally in the L1-5 vertebrae for each image volume. The optimal safe CBT trajectory was created by maximizing both the screw length and the cortical bone contact with the screw. Easily identifiable anatomical surface landmarks for the start point and trajectory that best allowed the reproducible idealized screw position were determined. An in vivo validation of the determined landmarks from the ex vivo study was then performed in 10 patients. Placement of virtual "test" cortical bone trajectory screws was simulated with the surgeon blinded to the real-time image-guided navigation, and the placement was evaluated. The surgeon then placed the definitive screw using image guidance.RESULTSFrom the ex vivo study, the optimized technique and landmarks were similar in the L1-4 vertebrae, whereas the L5 optimized technique was distinct. The in vivo validation yielded ideal, safe, and unsafe screws in 62%, 16%, and 22% of cases, respectively. A common reason for the nonidealized trajectories was the obscuration of patient anatomy secondary to severe degenerative changes.CONCLUSIONSCBT screws were placed ideally or safely 78% of the time in a virtual simulation model. A 22% rate of unsafe freehand trajectories suggests that the CBT technique requires use of image-guided navigation or x-ray guidance and that reliable freehand CBT screw insertion based on anatomical landmarks is not reliably feasible in the lumbar spine.