Publications by authors named "Maonian Wu"

Article Synopsis
  • The study investigates the interconnectedness of the uterus, bladder, and rectum, highlighting that issues in one organ can influence the others, while current diagnostic methods may lead to inaccuracies due to reliance on manual measurements.
  • Researchers developed a deep learning model using stress MRI images to classify the severity of pelvic organ prolapse, leveraging advanced techniques like vision transformer architecture and label masking training.
  • The model demonstrated high performance with average metrics of 0.86 Precision, 0.77 Kappa, 0.76 Recall, and a diagnosis time of just 0.38 seconds, outperforming traditional grading methods and providing interpretable results.
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Article Synopsis
  • The study aimed to evaluate an intelligent diagnostic model for pterygium using a fusion of advanced attention mechanisms with a lightweight MobileNetV2 structure for tri-classification.
  • It utilized a dataset of 1220 images from Nanjing Medical University and compared the performance of this model against conventional classification models, assessing factors like accuracy, Kappa value, and sensitivity.
  • The results showed that the MobileNetV2+Self-Attention model achieved high accuracy (92.77%) and excellent sensitivity (up to 99.47%), demonstrating its potential for efficient detection and severity assessment of pterygium.
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Aim: Conventional approaches to diagnosing common eye diseases using B-mode ultrasonography are labor-intensive and time-consuming, must requiring expert intervention for accuracy. This study aims to address these challenges by proposing an intelligence-assisted analysis five-classification model for diagnosing common eye diseases using B-mode ultrasound images.

Methods: This research utilizes 2064 B-mode ultrasound images of the eye to train a novel model integrating artificial intelligence technology.

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Background: Bladder prolapse is a common clinical disorder of pelvic floor dysfunction in women, and early diagnosis and treatment can help them recover. Pelvic magnetic resonance imaging (MRI) is one of the most important methods used by physicians to diagnose bladder prolapse; however, it is highly subjective and largely dependent on the clinical experience of physicians. The application of computer-aided diagnostic techniques to achieve a graded diagnosis of bladder prolapse can help improve its accuracy and shorten the learning curve.

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The effectiveness of sequence-to-sequence (seq2seq) models in natural language processing has been well-established over time, and recent studies have extended their utility by treating mathematical computing tasks as instances of machine translation and achieving remarkable results. However, our exploratory experiments have revealed that the seq2seq model, when employing a generic sorting strategy, is incapable of inferring on matrices of unseen rank, resulting in suboptimal performance. This paper aims to address this limitation by focusing on the matrix-to-sequence process and proposing a novel diagonal-based sorting.

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Purpose: We aimed to develop an artificial intelligence-based myopic maculopathy grading method using EfficientNet to overcome the delayed grading and diagnosis of different myopic maculopathy degrees.

Methods: The cooperative hospital provided 4642 healthy and myopic maculopathy color fundus photographs, comprising the four degrees of myopic maculopathy and healthy fundi. The myopic maculopathy grading models were trained using EfficientNet-B0 to EfficientNet-B7 models.

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Article Synopsis
  • Pterygium is a common eye condition that can lead to discomfort and impaired vision, highlighting the need for early and accurate diagnosis.
  • The paper discusses how artificial intelligence (AI), including techniques like machine learning and computer vision, is being leveraged to improve pterygium diagnosis through enhanced detection and classification methods.
  • It also evaluates the benefits and challenges of using AI in this context and explores future advancements that could lead to better diagnostic tools for pterygium.
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Aim: To conduct a classification study of high myopic maculopathy (HMM) using limited datasets, including tessellated fundus, diffuse chorioretinal atrophy, patchy chorioretinal atrophy, and macular atrophy, and minimize annotation costs, and to optimize the ALFA-Mix active learning algorithm and apply it to HMM classification.

Methods: The optimized ALFA-Mix algorithm (ALFA-Mix+) was compared with five algorithms, including ALFA-Mix. Four models, including ResNet18, were established.

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Purpose: Recently, the proportion of patients with high myopia has shown a continuous growing trend, more toward the younger age groups. This study aimed to predict the changes in spherical equivalent refraction (SER) and axial length (AL) in children using machine learning methods.

Methods: This study is a retrospective study.

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Purpose: To assess the value of an automated classification model for dry and wet macular degeneration based on the ConvNeXT model.

Methods: A total of 672 fundus images of normal, dry, and wet macular degeneration were collected from the Affiliated Eye Hospital of Nanjing Medical University and the fundus images of dry macular degeneration were expanded. The ConvNeXT three-category model was trained on the original and expanded datasets, and compared to the results of the VGG16, ResNet18, ResNet50, EfficientNetB7, and RegNet three-category models.

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A two-category model and a segmentation model of pterygium were proposed to assist ophthalmologists in establishing the diagnosis of ophthalmic diseases. A total of 367 normal anterior segment images and 367 pterygium anterior segment images were collected at the Affiliated Eye Hospital of Nanjing Medical University. AlexNet, VGG16, ResNet18, and ResNet50 models were used to train the two-category pterygium models.

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Purpose: To assess the value of automatic disc-fovea angle (DFA) measurement using the DeepLabv3+ segmentation model.

Methods: A total of 682 normal fundus image datasets were collected from the Eye Hospital of Nanjing Medical University. The following parts of the images were labeled and subsequently reviewed by ophthalmologists: optic disc center, macular center, optic disc area, and virtual macular area.

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Purpose: A six-category model of common retinal diseases is proposed to help primary medical institutions in the preliminary screening of the five common retinal diseases.

Methods: A total of 2,400 fundus images of normal and five common retinal diseases were provided by a cooperative hospital. Two six-category deep learning models of common retinal diseases based on the EfficientNet-B4 and ResNet50 models were trained.

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This study aims to implement and investigate the application of a special intelligent diagnostic system based on deep learning in the diagnosis of pterygium using anterior segment photographs. A total of 1,220 anterior segment photographs of normal eyes and pterygium patients were collected for training (using 750 images) and testing (using 470 images) to develop an intelligent pterygium diagnostic model. The images were classified into three categories by the experts and the intelligent pterygium diagnosis system: (i) the normal group, (ii) the observation group of pterygium, and (iii) the operation group of pterygium.

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Background: In the development of artificial intelligence in ophthalmology, the ophthalmic AI-related recognition issues are prominent, but there is a lack of research into people's familiarity with and their attitudes toward ophthalmic AI. This survey aims to assess medical workers' and other professional technicians' familiarity with, attitudes toward, and concerns about AI in ophthalmology.

Methods: This is a cross-sectional study design study.

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Aims: The lack of primary ophthalmologists in China results in the inability of basic-level hospitals to diagnose pterygium patients. To solve this problem, an intelligent-assisted lightweight pterygium diagnosis model based on anterior segment images is proposed in this study.

Methods: Pterygium is a common and frequently occurring disease in ophthalmology, and fibrous tissue hyperplasia is both a diagnostic biomarker and a surgical biomarker.

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Article Synopsis
  • This study addresses the shortage of ophthalmologists in China by proposing a five-category intelligent diagnosis model for common eye diseases like retinal vein occlusion and diabetic retinopathy.
  • The research involved collecting and analyzing 2,000 fundus images, training three different models, and achieving over 90% accuracy in diagnosing these conditions.
  • The findings will assist primary care doctors in delivering better diagnostic services to ophthalmologic patients, improving patient care overall.
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Introduction: In April 2018, the US Food and Drug Administration (FDA) approved the world's first artificial intelligence (AI) medical device for detecting diabetic retinopathy (DR), the IDx-DR. However, there is a lack of evaluation systems for DR intelligent diagnostic technology.

Methods: Five hundred color fundus photographs of diabetic patients were selected.

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