Abstract
Age is closely related to human health and disease risks. However, chronologically defined age often disagrees with biological age, primarily due to genetic and environmental variables. ...Identifying effective indicators for biological age in clinical practice and self-monitoring is important but currently lacking. The human lens accumulates age-related changes that are amenable to rapid and objective assessment. Here, using lens photographs from 20 to 96-year-olds, we develop LensAge to reflect lens aging via deep learning. LensAge is closely correlated with chronological age of relatively healthy individuals (R
2
> 0.80, mean absolute errors of 4.25 to 4.82 years). Among the general population, we calculate the LensAge index by contrasting LensAge and chronological age to reflect the aging rate relative to peers. The LensAge index effectively reveals the risks of age-related eye and systemic disease occurrence, as well as all-cause mortality. It outperforms chronological age in reflecting age-related disease risks (
p
< 0.001). More importantly, our models can conveniently work based on smartphone photographs, suggesting suitability for routine self-examination of aging status. Overall, our study demonstrates that the LensAge index may serve as an ideal quantitative indicator for clinically assessing and self-monitoring biological age in humans.
Visual impairment is a widespread public health issue that can have a negative impact on quality of life, education and socioeconomic development.1 The early stages of life, particularly early ...childhood (infancy and toddlerhood), are crucial periods for visual development, during which detection and treatment of ocular pathology can prevent irreversible vision loss.2,3 Unfortunately, young children are often unable to complain of ocular symptoms or unwilling to participate in standard vision tests, making it challenging to assess their visual functions. The first is the high requirements for the stability of the system due to the difficulty to collect qualified phenotypic data of young children in chaotic environments with interference or noise. ACKNOWLEDGEMENTS This study was funded by the National Natural Science Foundation of China (grant numbers: 82171035 and 91846109 to H.L.), the Science and Technology Planning Projects of Guangdong Province (grant number: 2021B1111610006 to H.L.), the Key-Area Research and Development of Guangdong Province (grant number: 2020B1111190001 to H.L.), the Guangzhou Basic and Applied Basic Research Project (grant number: 2022020328 to H.L.), China Postdoctoral Science Foundation (grant number: 2022M713589 to W.C.), the Fundamental Research Funds of the State Key Laboratory of Ophthalmology (grant number: 2022QN10 to W.C.) and Hainan Province Clinical Medical Center (H.L.).
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.
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.
Middle and outer ear diseases are common otological diseases worldwide. Otoscopy and otoendoscopy examinations 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 otoscopy 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.
AimTo investigate the association of floor area ratio (FAR), an indicator of built environments, and myopia onset.MethodsThis prospective cohort study recruited 136 753 children aged 6–10 years from ...108 schools in Shenzhen, China at baseline (2016–2017). Refractive power was measured with non-cycloplegic autorefraction over a 2-year follow-up period. FAR was objectively evaluated using geographical information system technology. Mixed-effects logistic regression models were constructed to examine the association of FAR with a 2-year cumulative incidence of myopia among individuals without baseline myopia; multiple linear regression model, with a 2-year cumulative incidence rate of myopia at each school.ResultsOf 101 624 non-myopic children (56.3% boys; mean (SE) age, 7.657±1.182 years) included in the study, 26 391 (26.0%) of them developed myopia after 2 years. In the individual-level analysis adjusting for demographic, socioeconomic and greenness factors, an IQR in FAR was associated with a decreased risk of 2-year myopia incidence (OR 0.898, 95% CI 0.866 to 0.932, p<0.001). Similar findings were observed in the analysis additionally adjusted for genetic and behavioural factors (OR 0.821, 95% CI 0.766 to 0.880, p<0.001). In the school-level, an IQR increase in FAR was found to be associated with a 2.0% reduction in the 2-year incidence rate of myopia (95% CI 1.3% to 2.6%, p<0.001).ConclusionsExposure to higher FAR was associated with a decreased myopia incidence, providing insights into myopia prevention through school built environments in China.
Early detection of visual impairment is crucial but is frequently missed in young children, who are capable of only limited cooperation with standard vision tests. Although certain features of ...visually impaired children, such as facial appearance and ocular movements, can assist ophthalmic practice, applying these features to real-world screening remains challenging. Here, we present a mobile health (mHealth) system, the smartphone-based Apollo Infant Sight (AIS), which identifies visually impaired children with any of 16 ophthalmic disorders by recording and analyzing their gazing behaviors and facial features under visual stimuli. Videos from 3,652 children (≤48 months in age; 54.5% boys) were prospectively collected to develop and validate this system. For detecting visual impairment, AIS achieved an area under the receiver operating curve (AUC) of 0.940 in an internal validation set and an AUC of 0.843 in an external validation set collected in multiple ophthalmology clinics across China. In a further test of AIS for at-home implementation by untrained parents or caregivers using their smartphones, the system was able to adapt to different testing conditions and achieved an AUC of 0.859. This mHealth system has the potential to be used by healthcare professionals, parents and caregivers for identifying young children with visual impairment across a wide range of ophthalmic disorders.
The storage of facial images in medical records poses privacy risks due to the sensitive nature of the personal biometric information that can be extracted from such images. To minimize these risks, ...we developed a new technology, called the digital mask (DM), which is based on three-dimensional reconstruction and deep-learning algorithms to irreversibly erase identifiable features, while retaining disease-relevant features needed for diagnosis. In a prospective clinical study to evaluate the technology for diagnosis of ocular conditions, we found very high diagnostic consistency between the use of original and reconstructed facial videos (κ ≥ 0.845 for strabismus, ptosis and nystagmus, and κ = 0.801 for thyroid-associated orbitopathy) and comparable diagnostic accuracy (P ≥ 0.131 for all ocular conditions tested) was observed. Identity removal validation using multiple-choice questions showed that compared to image cropping, the DM could much more effectively remove identity attributes from facial images. We further confirmed the ability of the DM to evade recognition systems using artificial intelligence-powered re-identification algorithms. Moreover, use of the DM increased the willingness of patients with ocular conditions to provide their facial images as health information during medical treatment. These results indicate the potential of the DM algorithm to protect the privacy of patients' facial images in an era of rapid adoption of digital health technologies.
Purpose
Due to population ageing as well as the high prevalence of hypertension and age‐related macular degeneration (AMD) in elderly individuals, and the relationship between hypertension and AMD is ...unclear. Our research aimed to investigate the association between hypertension, wet AMD (wAMD) and the treatment strategy of wAMD patients affected by hypertension.
Methods
Data of wAMD patients at Zhongshan Ophthalmic Center, Sun Yat‐sen University, between 1 January 2002 and 30 June 2019, were extracted from the electronic healthcare information system. wAMD patients were divided into subgroups by hypertension status, age, sex, the need for vitrectomy surgery and the number of anti‐VEGF drug intravitreal injections that these were divided in 1–3 vs. >3 (available time from 1 January 2012 to 30 June 2019).
Results
A total of 3096 wAMD patients (41.7% female, 58.3% male) with an age range of 50–96 years (68.7 (SD 9.42) years) were included. wAMD was significantly associated with hypertension (p < 0.001). After adjustment for sex and age, Cox regression model showed a significant association between hypertension in wAMD patients and the number of injections (RR = 1.31, 95% CI: 1.13–1.50, p < 0.001). There was no significant association between hypertension and the need for vitrectomy (p = 0.82).
Conclusions
wet AMD was associated with hypertension status, and after the regular series of three injections, wAMD patients with hypertension were more likely to receive anti‐VEGF drug intravitreal injections than those without hypertension. These results may facilitate prospective research on the prevention of wAMD and contribute to the management of wAMD patients.
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