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Summary: Accurate detection and timely care for patients with high myopia present significant challenges. We developed a deep learning (DL) system enhanced by a self-supervised learning (SSL) approach to improve the automatic diagnosis of myopic maculopathy (MM). Using a dataset of 7,906 images from the Shanghai High Myopia Screening Project and a public validation set of 1,391 images from MMAC2023, our method significantly outperformed conventional techniques. Internally, it achieved 96.8% accuracy, 83.1% sensitivity, and 95.6% specificity, with AUC values of 0.982 and 0.999. Externally, it maintained 89.0% accuracy, 71.7% sensitivity, and 87.8% specificity, with AUC values of 0.978 and 0.973. The model’s Cohen’s kappa values exceeded 0.8, indicating substantial agreement with retinal experts. Our SSL-enhanced DL approach offers high accuracy and potential to enhance large-scale myopia screenings, demonstrating broader significance in improving early detection and treatment of MM.

Summary: Protein biomarkers can be used to characterize symptom classes, which describe the metabolic or immunodeficient state of patients during the progression of a specific disease. Recent literature has shown that machine learning methods can complement traditional clinical methods in identifying biomarkers. However, many machine learning frameworks only apply narrowly to a specific archetype or subset of diseases. In this paper, we propose a feature extractor which can discover protein biomarkers for a wide variety of classification problems. The feature extractor uses a special type of deep learning model, which discovers a latent space that allows for optimal class separation and enhanced class cluster identity. The extracted biomarkers can then be used to train highly accurate supervised learning models. We apply our methods to a dataset involving COVID-19 patients and another involving scleroderma patients, to demonstrate improved class separation and reduced false discovery rates compared to results obtained using traditional models.

Summary: Accurate identification of intussusception in children is critical for timely non-surgical management. We propose an end-to-end artificial intelligence algorithm, the Children Intussusception Diagnosis Network (CIDNet) system, that utilizes ultrasound images to rapidly diagnose intussusception. 9999 ultrasound images of 4154 pediatric patients were divided into training, validation, test, and independent reader study datasets. The independent reader study cohort was used to compare the diagnostic performance of the CIDNet system to six radiologists. Performance was evaluated using, among others, balance accuracy (BACC) and area under the receiver operating characteristic curve (AUC). The CIDNet system performed the best in diagnosing intussusception with a BACC of 0.8464 and AUC of 0.9716 in the test dataset compared to other deep learning algorithms. The CIDNet system compared favorably with expert radiologists by outstanding identification performance and robustness (BACC:0.9297; AUC:0.9769). CIDNet is a stable and precise technological tool for identifying intussusception in ultrasound scans of children.

Summary: The research of AI-assisted breast diagnosis has primarily been based on static images. It is unclear whether it represents the best diagnosis image.To explore the method of capturing complementary responsible frames from breast ultrasound screening by using artificial intelligence. We used feature entropy breast network (FEBrNet) to select responsible frames from breast ultrasound screenings and compared the diagnostic performance of AI models based on FEBrNet-recommended frames, physician-selected frames, 5-frame interval-selected frames, all frames of video, as well as that of ultrasound and mammography specialists. The AUROC of AI model based on FEBrNet-recommended frames outperformed other frame set based AI models, as well as ultrasound and mammography physicians, indicating that FEBrNet can reach level of medical specialists in frame selection.FEBrNet model can extract video responsible frames for breast nodule diagnosis, whose performance is equivalent to the doctors selected responsible frames.

Summary: Mathematical models of cardiac electrical activity are one of the most important tools for elucidating information about heart diagnostics. In this paper, we present an efficient mathematical formulation for this modeling simple enough to be easily parameterized and rich enough to provide realistic signals. It relies on a five dipole representation of the cardiac electric source, each one associated with the well-known waves of the electrocardiogram signal. Beyond the physical basis of the model, the parameters are physiologically interpretable as they characterize the wave shape, similar to what a physician would look for in signals, thus making them very useful in diagnosis. The model accurately reproduces the electrocardiogram signals of any diseased or healthy heart. This new discovery represents a significant advance in electrocardiography research. It is especially useful for diagnosis, patient follow-up or decision-making on new therapies; is also a promising tool for well-performing, transparent and interpretable AI approaches.

Accurate detection and timely care for patients with high myopia present significant challenges. We developed a deep learning (DL) system enhanced by a self-supervised learning (SSL) approach to improve the automatic diagnosis of myopic maculopathy (MM). Using a dataset of 7,906 images from the Shanghai High Myopia Screening Project and a public validation set of 1,391 images from MMAC2023, our method significantly outperformed conventional techniques. Internally, it achieved 96.8% accuracy, 83.1% sensitivity, and 95.6% specificity, with AUC values of 0.982 and 0.999. Externally, it maintained 89.0% accuracy, 71.7% sensitivity, and 87.8% specificity, with AUC values of 0.978 and 0.973. The model's Cohen's kappa values exceeded 0.8, indicating substantial agreement with retinal experts. Our SSL-enhanced DL approach offers high accuracy and potential to enhance large-scale myopia screenings, demonstrating broader significance in improving early detection and treatment of MM., Competing Interests: The authors declare no competing interests., (© 2024 The Authors.)

Summary: Bladder cancer (BC) is the sixth leading cause of death by cancer. Depending on the invasiveness of tumors, patients with BC will undergo surgery and surveillance lifelong, owing the high rate of recurrence and progression. In this context, the development of strategies to support non-invasive BC diagnosis is focusing attention. Voltammetric electronic tongue (VET) has been demonstrated to be of use in the analysis of biofluids. Here, we present the implementation of a VET to study 207 urines to discriminate BC and non-BC for diagnosis and surveillance to detect recurrences. Special attention has been paid to the experimental setup to improve reproducibility in the measurements. PLSDA analysis together with variable selection provided a model with high sensitivity, specificity, and area under the ROC curve AUC (0.844, 0.882, and 0.917, respectively). These results pave the way for the development of non-invasive low-cost and easy-to-use strategies to support BC diagnosis and follow-up.

Summary: Automated seizure detection in long-term video-EEG recordings is far from being integrated into common clinical practice. Here, we leverage classical and state-of-the-art complexity measures to robustly and automatically detect seizures from scalp recordings. Brain activity is scored through eight features, encompassing traditional time domain and novel measures of recurrence. A binary classification algorithm tailored to treat unbalanced dataset is used to determine whether a time window is ictal or non-ictal from its features. The application of the algorithm on a cohort of ten adult patients with focal refractory epilepsy indicates sensitivity, specificity, and accuracy of 90%, along with a true alarm rate of 95% and less than four false alarms per day. The proposed approach emphasizes ictal patterns against noisy background without the need of data preprocessing. Finally, we benchmark our approach against previous studies on two publicly available datasets, demonstrating the good performance of our algorithm.

Protein biomarkers can be used to characterize symptom classes, which describe the metabolic or immunodeficient state of patients during the progression of a specific disease. Recent literature has shown that machine learning methods can complement traditional clinical methods in identifying biomarkers. However, many machine learning frameworks only apply narrowly to a specific archetype or subset of diseases. In this paper, we propose a feature extractor which can discover protein biomarkers for a wide variety of classification problems. The feature extractor uses a special type of deep learning model, which discovers a latent space that allows for optimal class separation and enhanced class cluster identity. The extracted biomarkers can then be used to train highly accurate supervised learning models. We apply our methods to a dataset involving COVID-19 patients and another involving scleroderma patients, to demonstrate improved class separation and reduced false discovery rates compared to results obtained using traditional models., Competing Interests: The authors declare no competing interests., (© 2023 The Authors.)

Accurate identification of intussusception in children is critical for timely non-surgical management. We propose an end-to-end artificial intelligence algorithm, the Children Intussusception Diagnosis Network (CIDNet) system, that utilizes ultrasound images to rapidly diagnose intussusception. 9999 ultrasound images of 4154 pediatric patients were divided into training, validation, test, and independent reader study datasets. The independent reader study cohort was used to compare the diagnostic performance of the CIDNet system to six radiologists. Performance was evaluated using, among others, balance accuracy (BACC) and area under the receiver operating characteristic curve (AUC). The CIDNet system performed the best in diagnosing intussusception with a BACC of 0.8464 and AUC of 0.9716 in the test dataset compared to other deep learning algorithms. The CIDNet system compared favorably with expert radiologists by outstanding identification performance and robustness (BACC:0.9297; AUC:0.9769). CIDNet is a stable and precise technological tool for identifying intussusception in ultrasound scans of children., Competing Interests: The authors declare no competing interests., (© 2023 The Author(s).)

The research of AI-assisted breast diagnosis has primarily been based on static images. It is unclear whether it represents the best diagnosis image.To explore the method of capturing complementary responsible frames from breast ultrasound screening by using artificial intelligence. We used feature entropy breast network (FEBrNet) to select responsible frames from breast ultrasound screenings and compared the diagnostic performance of AI models based on FEBrNet-recommended frames, physician-selected frames, 5-frame interval-selected frames, all frames of video, as well as that of ultrasound and mammography specialists. The AUROC of AI model based on FEBrNet-recommended frames outperformed other frame set based AI models, as well as ultrasound and mammography physicians, indicating that FEBrNet can reach level of medical specialists in frame selection.FEBrNet model can extract video responsible frames for breast nodule diagnosis, whose performance is equivalent to the doctors selected responsible frames., Competing Interests: The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article., (© 2022 The Author(s).)

Mathematical models of cardiac electrical activity are one of the most important tools for elucidating information about heart diagnostics. In this paper, we present an efficient mathematical formulation for this modeling simple enough to be easily parameterized and rich enough to provide realistic signals. It relies on a five dipole representation of the cardiac electric source, each one associated with the well-known waves of the electrocardiogram signal. Beyond the physical basis of the model, the parameters are physiologically interpretable as they characterize the wave shape, similar to what a physician would look for in signals, thus making them very useful in diagnosis. The model accurately reproduces the electrocardiogram signals of any diseased or healthy heart. This new discovery represents a significant advance in electrocardiography research. It is especially useful for diagnosis, patient follow-up or decision-making on new therapies; is also a promising tool for well-performing, transparent and interpretable AI approaches., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Author(s).)

Bladder cancer (BC) is the sixth leading cause of death by cancer. Depending on the invasiveness of tumors, patients with BC will undergo surgery and surveillance lifelong, owing the high rate of recurrence and progression. In this context, the development of strategies to support non-invasive BC diagnosis is focusing attention. Voltammetric electronic tongue (VET) has been demonstrated to be of use in the analysis of biofluids. Here, we present the implementation of a VET to study 207 urines to discriminate BC and non-BC for diagnosis and surveillance to detect recurrences. Special attention has been paid to the experimental setup to improve reproducibility in the measurements. PLSDA analysis together with variable selection provided a model with high sensitivity, specificity, and area under the ROC curve AUC (0.844, 0.882, and 0.917, respectively). These results pave the way for the development of non-invasive low-cost and easy-to-use strategies to support BC diagnosis and follow-up., Competing Interests: The authors declare no conflict of interest., (© 2022 The Author(s).)

Automated seizure detection in long-term video-EEG recordings is far from being integrated into common clinical practice. Here, we leverage classical and state-of-the-art complexity measures to robustly and automatically detect seizures from scalp recordings. Brain activity is scored through eight features, encompassing traditional time domain and novel measures of recurrence. A binary classification algorithm tailored to treat unbalanced dataset is used to determine whether a time window is ictal or non-ictal from its features. The application of the algorithm on a cohort of ten adult patients with focal refractory epilepsy indicates sensitivity, specificity, and accuracy of 90%, along with a true alarm rate of 95% and less than four false alarms per day. The proposed approach emphasizes ictal patterns against noisy background without the need of data preprocessing. Finally, we benchmark our approach against previous studies on two publicly available datasets, demonstrating the good performance of our algorithm., Competing Interests: The authors declare no competing interests. We confirm that we have read the Journal position on issues involved in ethical publication and affirm that this report is consistent with those guidelines., (© 2020 The Authors.)

Background: Parkinson's disease (PD) gradually degrades the functionality of the brain. Because of its relevance to the abnormality of the brain, electroencephalogram (EEG) signal is used for the early detection of this disease. This paper introduces a novel computer-aided diagnosis method to detect PD, which is an efficient deep learning method based on a pooling-based deep recurrent neural network (PDRNN). Therefore, the purpose of this study is to detect Parkinson's disease based on deep recurrent neural network of EEG signal., Methods: The EEG signals of 20 patients with Parkinson's disease and 20 healthy people in Henan Provincial People's Hospital (People's Hospital of Zhengzhou University) were examined, and a PDRNN learning method was applied on the dataset for managing the demand of the traditional feature presentation step., Results: The suggested DPRNN network gives the precision, sensitivity and specificity of 88.31%, 84.84% and 91.81%, respectively. Nevertheless, 11.28% of the healthy cases are wrongly categorized in Parkinson class. Also, 11.49% percent of Parkinson cases are classified wrongly in the healthy class., Conclusions: The experimental model has high efficiency and can be used as a reliable tool for clinical PD detection. In future research, more cases should be used to test and develop the proposed model., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/atm-20-5100). The authors have no conflicts of interest to declare., (2020 Annals of Translational Medicine. All rights reserved.)