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Abstract Background Manual cytological diagnosis for early esophageal squamous cell carcinoma (early ESCC) and high‐grade intraepithelial neoplasia (HGIN) is unsatisfactory. Herein, we have introduced an artificial intelligence (AI)‐assisted cytological diagnosis for such lesions. Methods Low‐grade squamous intraepithelial lesion or worse was set as the diagnostic threshold for AI‐assisted diagnosis. The performance of AI‐assisted diagnosis was evaluated and compared to that of manual diagnosis. Feasibility in large‐scale screening was also assessed. Results AI‐assisted diagnosis for abnormal cells was superior to manual reading by presenting a higher efficiency for each slide (50.9 ± 0.8 s vs 236.8 ± 3.9 s, p = 1.52 × 10−76) and a better interobserver agreement (93.27% [95% CI, 92.76%–93.74%] vs 65.29% [95% CI, 64.35%–66.22%], p = 1.03 × 10−84). AI‐assisted detection showed a higher diagnostic accuracy (96.89% [92.38%–98.57%] vs 72.54% [65.85%–78.35%], p = 1.42 × 10−14), sensitivity (99.35% [95.92%–99.97%] vs 68.39% [60.36%–75.48%], p = 7.11 × 10−15), and negative predictive value (NPV) (97.06% [82.95%–99.85%] vs 40.96% [30.46%–52.31%], p = 1.42 × 10−14). Specificity and positive predictive value (PPV) were not significantly differed. AI‐assisted diagnosis demonstrated a smaller proportion of participants of interest (3.73%, [79/2117] vs.12.84% [272/2117], p = 1.59 × 10−58), a higher consistence between cytology and endoscopy (40.51% [32/79] vs. 12.13% [33/272], p = 1.54 × 10−8), specificity (97.74% [96.98%–98.32%] vs 88.52% [87.05%–89.84%], p = 3.19 × 10−58), and PPV (40.51% [29.79%–52.15%] vs 12.13% [8.61%–16.75%], p = 1.54 × 10−8) in community‐based screening. Sensitivity and NPV were not significantly differed. AI‐assisted diagnosis as primary screening significantly reduced average cost for detecting positive cases. Conclusion Our study provides a novel cytological method for detecting and screening early ESCC and HGIN.

Background: Manual cytological diagnosis for early esophageal squamous cell carcinoma (early ESCC) and high‐grade intraepithelial neoplasia (HGIN) is unsatisfactory. Herein, we have introduced an artificial intelligence (AI)‐assisted cytological diagnosis for such lesions. Methods: Low‐grade squamous intraepithelial lesion or worse was set as the diagnostic threshold for AI‐assisted diagnosis. The performance of AI‐assisted diagnosis was evaluated and compared to that of manual diagnosis. Feasibility in large‐scale screening was also assessed. Results: AI‐assisted diagnosis for abnormal cells was superior to manual reading by presenting a higher efficiency for each slide (50.9 ± 0.8 s vs 236.8 ± 3.9 s, p = 1.52 × 10−76) and a better interobserver agreement (93.27% [95% CI, 92.76%–93.74%] vs 65.29% [95% CI, 64.35%–66.22%], p = 1.03 × 10−84). AI‐assisted detection showed a higher diagnostic accuracy (96.89% [92.38%–98.57%] vs 72.54% [65.85%–78.35%], p = 1.42 × 10−14), sensitivity (99.35% [95.92%–99.97%] vs 68.39% [60.36%–75.48%], p = 7.11 × 10−15), and negative predictive value (NPV) (97.06% [82.95%–99.85%] vs 40.96% [30.46%–52.31%], p = 1.42 × 10−14). Specificity and positive predictive value (PPV) were not significantly differed. AI‐assisted diagnosis demonstrated a smaller proportion of participants of interest (3.73%, [79/2117] vs.12.84% [272/2117], p = 1.59 × 10−58), a higher consistence between cytology and endoscopy (40.51% [32/79] vs. 12.13% [33/272], p = 1.54 × 10−8), specificity (97.74% [96.98%–98.32%] vs 88.52% [87.05%–89.84%], p = 3.19 × 10−58), and PPV (40.51% [29.79%–52.15%] vs 12.13% [8.61%–16.75%], p = 1.54 × 10−8) in community‐based screening. Sensitivity and NPV were not significantly differed. AI‐assisted diagnosis as primary screening significantly reduced average cost for detecting positive cases. Conclusion: Our study provides a novel cytological method for detecting and screening early ESCC and HGIN. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI)-driven software has been developed and become commercially available within the past few years for the detection of intracranial hemorrhage (ICH) and chronic cerebral microbleeds (CMBs). However, there is currently no systematic review that summarizes all of these tools or provides pooled estimates of their performance. Methods: In this PROSPERO-registered, PRISMA compliant systematic review, we sought to compile and review all MEDLINE and EMBASE published studies that have developed and/or tested AI algorithms for ICH detection on non-contrast CT scans (NCCTs) or MRI scans and CMBs detection on MRI scans. Results: In total, 40 studies described AI algorithms for ICH detection in NCCTs/MRIs and 19 for CMBs detection in MRIs. The overall sensitivity, specificity, and accuracy were 92.06%, 93.54%, and 93.46%, respectively, for ICH detection and 91.6%, 93.9%, and 92.7% for CMBs detection. Some of the challenges encountered in the development of these algorithms include the laborious work of creating large, labeled and balanced datasets, the volumetric nature of the imaging examinations, the fine tuning of the algorithms, and the reduction in false positives. Conclusions: Numerous AI-driven software tools have been developed over the last decade. On average, they are characterized by high performance and expert-level accuracy for the diagnosis of ICH and CMBs. As a result, implementing these tools in clinical practice may improve workflow and act as a failsafe for the detection of such lesions. Registration-URL: https://www.crd.york.ac.uk/prospero/ Unique Identifier: CRD42021246848. [ABSTRACT FROM AUTHOR]

Osteosarcoma is a malignant osteosarcoma that is extremely harmful to human health. Magnetic resonance imaging (MRI) technology is one of the commonly used methods for the imaging examination of osteosarcoma. Due to the large amount of osteosarcoma MRI image data and the complexity of detection, manual identification of osteosarcoma in MRI images is a time-consuming and labor-intensive task for doctors, and it is highly subjective, which can easily lead to missed and misdiagnosed problems. AI medical image-assisted diagnosis alleviates this problem. However, the brightness of MRI images and the multi-scale of osteosarcoma make existing studies still face great challenges in the identification of tumor boundaries. Based on this, this study proposed a prior guidance-based assisted segmentation method for MRI images of osteosarcoma, which is based on the few-shot technique for tumor segmentation and fine fitting. It not only solves the problem of multi-scale tumor localization, but also greatly improves the recognition accuracy of tumor boundaries. First, we preprocessed the MRI images using prior generation and normalization algorithms to reduce model performance degradation caused by irrelevant regions and high-level features. Then, we used a prior-guided feature abdominal muscle network to perform small-sample segmentation of tumors of different sizes based on features in the processed MRI images. Finally, using more than 80,000 MRI images from the Second Xiangya Hospital for experiments, the DOU value of the method proposed in this paper reached 0.945, which is at least 4.3% higher than other models in the experiment. We showed that our method specifically has higher prediction accuracy and lower resource consumption. [ABSTRACT FROM AUTHOR]

Abstract Background To improve the diagnostic accuracy of axillary lymph node (LN) metastasis in breast cancer patients using 2-[18F]FDG-PET/CT, we constructed an artificial intelligence (AI)-assisted diagnosis system that uses deep-learning technologies. Materials and methods Two clinicians and the new AI system retrospectively analyzed and diagnosed 414 axillae of 407 patients with biopsy-proven breast cancer who had undergone 2-[18F]FDG-PET/CT before a mastectomy or breast-conserving surgery with a sentinel lymph node (LN) biopsy and/or axillary LN dissection. We designed and trained a deep 3D convolutional neural network (CNN) as the AI model. The diagnoses from the clinicians were blended with the diagnoses from the AI model to improve the diagnostic accuracy. Results Although the AI model did not outperform the clinicians, the diagnostic accuracies of the clinicians were considerably improved by collaborating with the AI model: the two clinicians' sensitivities of 59.8% and 57.4% increased to 68.6% and 64.2%, respectively, whereas the clinicians' specificities of 99.0% and 99.5% remained unchanged. Conclusions It is expected that AI using deep-learning technologies will be useful in diagnosing axillary LN metastasis using 2-[18F]FDG-PET/CT. Even if the diagnostic performance of AI is not better than that of clinicians, taking AI diagnoses into consideration may positively impact the overall diagnostic accuracy.

Osteosarcoma is a malignant tumor derived from primitive osteogenic mesenchymal cells, which is extremely harmful to the human body and has a high mortality rate. Early diagnosis and treatment of this disease is necessary to improve the survival rate of patients, and MRI is an effective tool for detecting osteosarcoma. However, due to the complex structure and variable location of osteosarcoma, cancer cells are highly heterogeneous and prone to aggregation and overlap, making it easy for doctors to inaccurately predict the area of the lesion. In addition, in developing countries lacking professional medical systems, doctors need to examine mass of osteosarcoma MRI images of patients, which is time-consuming and inefficient, and may result in misjudgment and omission. For the sake of reducing labor cost and improve detection efficiency, this paper proposes an Attention Condenser-based MRI image segmentation system for osteosarcoma (OMSAS), which can help physicians quickly locate the lesion area and achieve accurate segmentation of the osteosarcoma tumor region. Using the idea of AttendSeg, we constructed an Attention Condenser-based residual structure network (ACRNet), which greatly reduces the complexity of the structure and enables smaller hardware requirements while ensuring the accuracy of image segmentation. The model was tested on more than 4000 samples from two hospitals in China. The experimental results demonstrate that our model has higher efficiency, higher accuracy and lighter structure for osteosarcoma MRI image segmentation compared to other existing models.

Osteosarcoma is a malignant osteosarcoma that is extremely harmful to human health. Magnetic resonance imaging (MRI) technology is one of the commonly used methods for the imaging examination of osteosarcoma. Due to the large amount of osteosarcoma MRI image data and the complexity of detection, manual identification of osteosarcoma in MRI images is a time-consuming and labor-intensive task for doctors, and it is highly subjective, which can easily lead to missed and misdiagnosed problems. AI medical image-assisted diagnosis alleviates this problem. However, the brightness of MRI images and the multi-scale of osteosarcoma make existing studies still face great challenges in the identification of tumor boundaries. Based on this, this study proposed a prior guidance-based assisted segmentation method for MRI images of osteosarcoma, which is based on the few-shot technique for tumor segmentation and fine fitting. It not only solves the problem of multi-scale tumor localization, but also greatly improves the recognition accuracy of tumor boundaries. First, we preprocessed the MRI images using prior generation and normalization algorithms to reduce model performance degradation caused by irrelevant regions and high-level features. Then, we used a prior-guided feature abdominal muscle network to perform small-sample segmentation of tumors of different sizes based on features in the processed MRI images. Finally, using more than 80,000 MRI images from the Second Xiangya Hospital for experiments, the DOU value of the method proposed in this paper reached 0.945, which is at least 4.3% higher than other models in the experiment. We showed that our method specifically has higher prediction accuracy and lower resource consumption.

Background: To improve the diagnostic accuracy of axillary lymph node (LN) metastasis in breast cancer patients using 2-[18F]FDG-PET/CT, we constructed an artificial intelligence (AI)-assisted diagnosis system that uses deep-learning technologies. Materials and methods: Two clinicians and the new AI system retrospectively analyzed and diagnosed 414 axillae of 407 patients with biopsy-proven breast cancer who had undergone 2-[18F]FDG-PET/CT before a mastectomy or breast-conserving surgery with a sentinel lymph node (LN) biopsy and/or axillary LN dissection. We designed and trained a deep 3D convolutional neural network (CNN) as the AI model. The diagnoses from the clinicians were blended with the diagnoses from the AI model to improve the diagnostic accuracy. Results: Although the AI model did not outperform the clinicians, the diagnostic accuracies of the clinicians were considerably improved by collaborating with the AI model: the two clinicians' sensitivities of 59.8% and 57.4% increased to 68.6% and 64.2%, respectively, whereas the clinicians' specificities of 99.0% and 99.5% remained unchanged. Conclusions: It is expected that AI using deep-learning technologies will be useful in diagnosing axillary LN metastasis using 2-[18F]FDG-PET/CT. Even if the diagnostic performance of AI is not better than that of clinicians, taking AI diagnoses into consideration may positively impact the overall diagnostic accuracy. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) monitoring and objective seizure identification is an essential clinical investigation for some patients with epilepsy. Accurate annotation is done through a time-consuming process by EEG specialists. Computer-assisted systems for seizure detection currently lack extensive clinical utility due to the retrospective, patient-specific, and/or irreproducible studies that result in low sensitivity or high false positives in clinical tests. We aim to significantly reduce the time and resources on data annotation by demonstrating a continental generalization of seizure detection that balances sensitivity and specificity. This is a prospective inference test of artificial intelligence on nearly 14,590 hours of adult EEG data from patients with epilepsy between 2011 and 2019 in a hospital in Sydney, Australia. The inference set includes patients with different types and frequencies of seizures across a wide range of ages and EEG recording hours. Artificial intelligence (AI) is a convolutional long short-term memory network that is trained on a USA-based dataset. The Australian set is about 16 times larger than the US training dataset with very long interictal periods (between seizures), which is way more realistic than the training set and makes our false positives highly reliable. We validated our inference model in an AI-assisted mode with a human expert arbiter and a result review panel of expert neurologists and EEG specialists on 66 sessions to demonstrate achievement of the same performance with over an order-of-magnitude reduction in time. Our inference on 1006 EEG recording sessions on the Australian dataset achieved 76.68% with nearly 56 [0, 115] false alarms per 24 hours on average, against legacy ground-truth annotations by human experts, conducted independently over nine years. Our pilot test of 66 sessions with a human arbiter, and reviewed ground truth by a panel of experts, confirmed an identical human performance of 92.19% with an AI-assisted system, while the time requirements reduced significantly from 90 to 7.62 min on average. Accurate and objective seizure counting is an important factor in epilepsy. An AI-assisted system can help improve efficiency and accuracy alongside human experts, particularly in low and middle-income countries with limited expert human resources. • The first AI model tested at a large scale unseen epilepsy datasets. • The AI-assisted system shows the potential of discovering missed seizures. • The AI-assisted system achieves more than a ten-fold increase in time efficiency. [ABSTRACT FROM AUTHOR]