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Ophthalmology Ophthalmology

The 100 Most Cited Articles in Ophthalmology in Asia.

In Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)

PURPOSE : The aim of this study was to review the top 100 most-cited articles in Ophthalmology in Asia since 1970.

METHODS : The Scopus database was used to identify the top 100 most-cited ophthalmology articles published in ophthalmology (T100-Eye) and nonophthalmology (T100-General) journals.

RESULTS : The T100-Eye articles were published between 1982 and 2015, and T100-General from 1982 to 2017. T100-Eye had higher citations [median (range) = 317 (249-1326)] than T100-General [158 (105-2628)], but T100-General were published in journals with higher impact factor (IF) than T100-Eye (median IF= 5.5 vs 4.4) and produced more landmark papers (3 vs 1 articles that were cited >1000 times). Fifty-five % of T100-Eye were published in 3 journals: Ophthalmology (n = 22), Investigative Ophthalmology and Visual Science (n = 17), and American Journal of Ophthalmology (n = 16). T100-Eye had 88 original research articles and 12 reviews, whereas T100-General had 84 original research and 16 reviews. The most-frequent studied disease categories were myopia (n = 16) and age-related macular degeneration (n = 15) in T100-Eye and diabetic retinopathy (n = 24) and glaucoma (n = 16) in T100-General. Japan and Singapore contributed most to T100-Eye (n = 42, n = 17) and T100-General (n = 36, n = 26) articles. More than 80% and 95% of first and last authors were male in both lists. Emerging research topics were optical coherence tomography in T100-Eye and artificial intelligence in T100-General.

CONCLUSIONS : Our citation analysis reveals differences in the focus of research topics of top-cited ophthalmology articles published in ophthalmology and nonophthalmology journals in Asia. It highlights that certain eye diseases are studied more in Asia and shows the contribution of specific countries to highly cited publications in ophthalmology research in Asia.

Koh Barry Moses Quan Ren, Banu Riswana, Sabanayagam Charumathi


oncology Oncology

Crystal Bone: Predicting Short-Term Fracture Risk From Electronic Health Records With Deep Learning.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Fractures due to osteoporosis and low bone mass are common and give rise to significant clinical, personal, and economic burden. Even after a fracture occurs, high fracture risk remains widely underdiagnosed and undertreated. Common fracture risk assessment tools utilize a subset of clinical risk factors for prediction, and often require manual data entry. Furthermore, these tools predict risk over the long term, and do not explicitly provide short-term risk estimates necessary to identify patients likely to experience a fracture in the next 1-2 years.

OBJECTIVE : The goal of this study was to develop and evaluate an algorithmic approach to the identification of patients at risk of fracture in the next 1-2 years. In order to address the aforementioned limitations of current prediction tools, this approach focuses on a short-term timeframe, automated data entry, and the use of longitudinal data to inform the predictions.

METHODS : Using electronic health record (EHR) data from over 1M patients, we developed Crystal Bone, a method that applies machine learning techniques from Natural Language Processing to the temporal nature of patient histories to generate short-term fracture risk predictions. Similar to how language models predict the next word in a given sentence or the topic of a document, Crystal Bone predicts whether a patient's future trajectory might contain a fracture event, or whether the "signature" of the patient's journey is similar to that of a typical future fracture patient.

RESULTS : The proposed models accurately predict fracture risk in the next 1-2 years for patients aged over 50 years (area under the receiver operating characteristics curve [AUROC]=0.81 in a holdout set with 192,590 patients). These algorithms outperform the experimental baselines (AUROC=0.67) and have shown meaningful improvements when compared to a retrospective approximation of human-level performance, such as correctly identifying 9649 of 13,765 (70%) at-risk patients who did not receive any preventative bone-health-related medical interventions from their physicians.

CONCLUSIONS : These findings indicate that it is possible to use a patient's unique medical history as it changes over time to predict the risk of short-term fracture. Validating and applying such a tool within the healthcare system could enable automated and widespread prediction of this risk and may help with identification of patients at very high risk of fracture.

Almog Yasmeen Adar, Rai Angshu, Zhang Patrick, Moulaison Amanda, Powell Ross, Mishra Anirban, Weinberg Kerry, Hamilton Celeste, Oates Mary, McCloskey Eugene, Cummings Steven R


General General

Deep Learning for Diagnosis and Segmentation of Pneumothorax: The Results on The Kaggle Competition and Validation Against Radiologists.

In IEEE journal of biomedical and health informatics

Pneumothorax is potentially a life-threatening disease that requires urgent diagnosis and treatment. The chest X-ray is the diagnostic modality of choice when the pneumothorax is suspected. Computer-aided diagnosis of pneumothorax has got a dramatic boost in the last years due to deep learning advances and the first public pneumothorax diagnosis competition with 15257 chest X-rays manually annotated by a team of 19 radiologists. This paper presents one of the top frameworks that participated in the competition. The framework investigates the benefits of combining the Unet convolutional neural network with various backbones, namely ResNet34, SE-ResNext50, SE-ResNext101, and DenseNet121. The paper presents a step-by-step instruction for the framework application, including data augmentation, and different pre- and post-processing steps. The performance of the framework was of 0.8574 measured in terms of the Dice coefficient. The second contribution of the paper is the comparison of the deep learning framework against three experienced radiologists on the pneumothorax detection and segmentation on challenging X-rays. We also evaluated how diagnostic confidence of radiologists affect the accuracy of the diagnosis and found out that the deep learning framework and radiologists find same X-rays to be easy/difficult to analyze (p-value <1e4). Finally, the methodology of all top-performing teams from the competition leaderboard was analyzed to find the consistent methodological patterns of accurate pneumothorax detection and segmentation.

Tolkachev Alexey, Sirazitdinov Ilyas, Kholiavchenko Maksym, Mustafaev Tamerlan, Ibragimov Bulat


Dermatology Dermatology

Digital Biopsy with Fluorescence Confocal Microscope for Effective Real-time Diagnosis of Prostate Cancer: A Prospective, Comparative Study.

In European urology oncology

BACKGROUND : A microscopic analysis of tissue is the gold standard for cancer detection. Hematoxylin-eosin (HE) for the reporting of prostate biopsy (PB) is conventionally based on fixation, processing, acquisition of glass slides, and analysis with an analog microscope by a local pathologist. Digitalization and real-time remote access to images could enhance the reporting process, and form the basis of artificial intelligence and machine learning. Fluorescence confocal microscopy (FCM), a novel optical technology, enables immediate digital image acquisition in an almost HE-like resolution without requiring conventional processing.

OBJECTIVE : The aim of this study is to assess the diagnostic ability of FCM for prostate cancer (PCa) identification and grading from PB.

DESIGN, SETTING, AND PARTICIPANTS : This is a prospective, comparative study evaluating FCM and HE for prostate tissue interpretation. PBs were performed (March to June 2019) at a single coordinating unit on consecutive patients with clinical and laboratory indications for assessment. FCM digital images (n = 427) were acquired immediately from PBs (from 54 patients) and stored; corresponding glass slides (n = 427) undergoing the conventional HE processing were digitalized and stored as well. A panel of four international pathologists with diverse background participated in the study and was asked to evaluate all images. The pathologists had no FCM expertise and were blinded to clinical data, HE interpretation, and each other's evaluation. All images, FCM and corresponding HE, were assessed for the presence or absence of cancer tissue and cancer grading, when appropriate. Reporting was gathered via a dedicated web platform.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS : The primary endpoint is to evaluate the ability of FCM to identify cancer tissue in PB cores (per-slice analysis). FCM outcomes are interpreted by agreement level with HE (K value). Additionally, either FCM or HE outcomes are assessed with interobserver agreement for cancer detection (presence vs absence of cancer) and for the discrimination between International Society of Urologic Pathologists (ISUP) grade = 1 and ISUP grade > 1 (secondary endpoint).

RESULTS AND LIMITATIONS : Overall, 854 images were evaluated from each pathologist. PCa detection of FCM was almost perfectly aligned with HE final reports (95.1% of correct diagnosis with FCM, κ = 0.84). Inter-rater agreement between pathologists was almost perfect for both HE and FCM for PCa detection (0.98 for HE, κ = 0.95; 0.95 for FCM, κ = 0.86); for cancer grade attribution, only a moderate agreement was reached for both HE and FCM (HE, κ = 0.47; FCM, κ = 0.49).

CONCLUSIONS : FCM provides a microscopic, immediate, and seemingly reliable diagnosis for PCa. The real-time acquisition of digital images-without requiring conventional processing-offers opportunities for immediate sharing and reporting. FCM is a promising tool for improvements in cancer diagnostic pathways.

PATIENT SUMMARY : Fluorescence confocal microscopy may provide an immediate, microscopic, and apparently reliable diagnosis of prostate cancer on prostate biopsy, overcoming the standard turnaround time of conventional processing and interpretation.

Rocco Bernardo, Sighinolfi Maria Chiara, Sandri Marco, Spandri Valentina, Cimadamore Alessia, Volavsek Metka, Mazzucchelli Roberta, Lopez-Beltran Antonio, Eissa Ahmed, Bertoni Laura, Azzoni Paola, Reggiani Bonetti Luca, Maiorana Antonino, Puliatti Stefano, Micali Salvatore, Paterlini Maurizio, Iseppi Andrea, Rocco Francesco, Pellacani Giovanni, Chester Johanna, Bianchi Giampaolo, Montironi Rodolfo


Digital pathology, Fluorescence confocal microscope, Prostate biopsy

Radiology Radiology

Temporal changes of COVID-19 pneumonia by mass evaluation using CT: a retrospective multi-center study.

In Annals of translational medicine

Background : Coronavirus disease 2019 (COVID-19) has widely spread worldwide and caused a pandemic. Chest CT has been found to play an important role in the diagnosis and management of COVID-19. However, quantitatively assessing temporal changes of COVID-19 pneumonia over time using CT has still not been fully elucidated. The purpose of this study was to perform a longitudinal study to quantitatively assess temporal changes of COVID-19 pneumonia.

Methods : This retrospective and multi-center study included patients with laboratory-confirmed COVID-19 infection from 16 hospitals between January 19 and March 27, 2020. Mass was used as an approach to quantitatively measure dynamic changes of pulmonary involvement in patients with COVID-19. Artificial intelligence (AI) was employed as image segmentation and analysis tool for calculating the mass of pulmonary involvement.

Results : A total of 581 confirmed patients with 1,309 chest CT examinations were included in this study. The median age was 46 years (IQR, 35-55; range, 4-87 years), and 311 (53.5%) patients were male. The mass of pulmonary involvement peaked on day 10 after the onset of initial symptoms. Furthermore, the mass of pulmonary involvement of older patients (>45 years) was significantly severer (P<0.001) and peaked later (day 11 vs. day 8) than that of younger patients (≤45 years). In addition, there were no significant differences in the peak time (day 10 vs. day 10) and median mass (P=0.679) of pulmonary involvement between male and female.

Conclusions : Pulmonary involvement peaked on day 10 after the onset of initial symptoms in patients with COVID-19. Further, pulmonary involvement of older patients was severer and peaked later than that of younger patients. These findings suggest that AI-based quantitative mass evaluation of COVID-19 pneumonia hold great potential for monitoring the disease progression.

Wang Chao, Huang Peiyu, Wang Lihua, Shen Zhujing, Lin Bin, Wang Qiyuan, Zhao Tongtong, Zheng Hanpeng, Ji Wenbin, Gao Yuantong, Xia Junli, Cheng Jianmin, Ma Jianbing, Liu Jun, Liu Yongqiang, Su Miaoguang, Ruan Guixiang, Shu Jiner, Ren Dawei, Zhao Zhenhua, Yao Weigen, Yang Yunjun, Liu Bo, Zhang Minming


Coronavirus disease 2019 (COVID-19), artificial intelligence (AI), chest CT, temporal changes

General General

The Use of AI for Thermal Emotion Recognition: A Review of Problems and Limitations in Standard Design and Data

ArXiv Preprint

With the increased attention on thermal imagery for Covid-19 screening, the public sector may believe there are new opportunities to exploit thermal as a modality for computer vision and AI. Thermal physiology research has been ongoing since the late nineties. This research lies at the intersections of medicine, psychology, machine learning, optics, and affective computing. We will review the known factors of thermal vs. RGB imaging for facial emotion recognition. But we also propose that thermal imagery may provide a semi-anonymous modality for computer vision, over RGB, which has been plagued by misuse in facial recognition. However, the transition to adopting thermal imagery as a source for any human-centered AI task is not easy and relies on the availability of high fidelity data sources across multiple demographics and thorough validation. This paper takes the reader on a short review of machine learning in thermal FER and the limitations of collecting and developing thermal FER data for AI training. Our motivation is to provide an introductory overview into recent advances for thermal FER and stimulate conversation about the limitations in current datasets.

Catherine Ordun, Edward Raff, Sanjay Purushotham