Assessment of Efficacy and Accuracy of Cervical Cytology Screening With Artificial Intelligence Assistive System.

The role of artificial intelligence (AI) in pathology offers many exciting new possibilities for improving patient care. This study contributes to this development by identifying the viability of the AICyte assistive system for cervical screening, and investigating the utility of the system in assisting with workflow and diagnostic capability. In this study, a novel scanner was developed using a Ruiqian WSI-2400, trademarked AICyte assistive system, to create an AI-generated gallery of the most diagnostically relevant images, objects of interest (OOI), and provide categorical assessment, according to Bethesda category, for cervical ThinPrep Pap slides. For validation purposes, 2 pathologists reviewed OOIs from 32,451 cases of ThinPrep Paps independently, and their interpretations were correlated with the original ThinPrep interpretations (OTPI). The analysis was focused on the comparison of reporting rates, correlation between cytological results and histologic follow-up findings, and the assessment of independent AICyte screening utility. Pathologists using the AICyte system had a mean reading time of 55.14 seconds for the first 3000 cases trending down to 12.90 seconds in the last 6000 cases. Overall average reading time was 22.23 seconds per case compared with a manual reading time approximation of 180 seconds. Usage of AICyte compared with OTPI had similar sensitivity (97.89% vs 97.89%) and a statistically significant increase in specificity (16.19% vs 6.77%) for the detection of cervical intraepithelial neoplsia 2 and above lesions. When AICyte was run alone at a 50% negative cutoff value, it was able to read slides with a sensitivity of 99.30% and a specificity of 9.87%. When AICyte was run independently at this cutoff value, no sole case of high-grade squamous intraepithelial lesions/squamous cell carcinoma squamous lesion was missed. AICyte can provide a potential tool to help pathologists in both diagnostic capability and efficiency, which remained reliable compared with the baseline standard. Also unique for AICyte is the development of a negative cutoff value for which AICyte can categorize cases as "not needed for review" to triage cases and lower pathologist workload. This is the largest case number study that pathologists reviewed OOI with an AI-assistive system. The study demonstrates that AI-assistive system can be broadly applied for cervical cancer screening.

AI-assisted system improves the work efficiency of cytologists via excluding cytology-negative slides and accelerating the slide interpretation.

Given the shortage of cytologists, women in low-resource regions had inequitable access to cervical cytology which plays an pivotal role in cervical cancer screening. Emerging studies indicated the potential of AI-assisted system in promoting the implementation of cytology in resource-limited settings. However, there is a deficiency in evaluating the aid of AI in the improvement of cytologists' work efficiency. This study aimed to evaluate the feasibility of AI in excluding cytology-negative slides and improve the efficiency of slide interpretation. Well-annotated slides were included to develop the classification model that was applied to classify slides in the validation group. Nearly 70% of validation slides were reported as negative by the AI system, and none of these slides were diagnosed as high-grade lesions by expert cytologists. With the aid of AI system, the average of interpretation time for each slide decreased from 3 minutes to 30 seconds. These findings suggested the potential of AI-assisted system in accelerating slide interpretation in the large-scale cervical cancer screening.

External carotid artery-radial artery graft-posterior cerebral artery bypass for complex vertebrobasilar aneurysms: efficacy and analysis of outcome in a single center.

The purpose of this research was to demonstrate the effectiveness and clinical outcome of an external carotid artery-radial artery graft-posterior cerebral artery (ECA-RAG-PCA) bypass in the treatment of complex vertebrobasilar artery aneurysms (VBANs) in a single-center retrospective study. An ECA-RAG-PCA bypass may be a last and very important option in the treatment of complex VBANs when conventional surgical clipping or endovascular interventions fail to achieve the desired outcome. This study retrospectively analyzed the clinical presentation, case characteristics, aneurysm location, size and morphology, choice of surgical strategy, complications, clinical follow-up, and prognosis of the patients enrolled. The data involved were analyzed by the appropriate statistical methods. A total of 24 patients with complex VBANs who met the criteria were included in this study. Eighteen (75.0%) were male and the mean age was 54.1 ± 8.83 years. The aneurysms were located in the vertebral artery, the basilar artery, and in the vertebrobasilar artery with simultaneous involvement. All patients underwent ECA-RAG-PCA bypass surgery via an extended middle cranial fossa approach, with 8 (33.3%) undergoing ECA-RAG-PCA bypass only, 3 (12.5%) undergoing ECA-RAG-PCA bypass combined with aneurysm partial trapping, and 12 (50.0%) undergoing ECA-RAG-PCA bypass combined with proximal occlusion of the parent artery. The average clinical follow-up was 22.0 ± 13.35 months. The patency rate of the high-flow bypass was 100%. At the final follow-up, 15 (62.5%) patients had complete occlusion of the aneurysm, 7 (29.2%) patients had subtotal occlusion of the aneurysm, and 2 (8.3%) patients had stable aneurysms. The rate of complete and subtotal occlusion of the aneurysm at the final follow-up was 91.7%. The clinical prognosis was good in 21 (87.5%) patients and no procedure-related deaths occurred. Analysis of the good and poor prognosis groups revealed a statistically significant difference in aneurysm size (P = 0.034, t-test). Combining the results of this study and the clinical experience of our center, we propose a surgical algorithm and strategy for the treatment of complex VBANs.The technical approach of ECA-RAG-PCA bypass for complex VBANs remains important, even in an era of rapid advances in endovascular intervention. When conventional surgical clipping or endovascular intervention has failed, an ECA-RAG-PCA bypass plays a role that cannot be abandoned and is a very important treatment option of last resort.

Study on Microsurgical Anatomy of External Branch of Superior Laryngeal Nerve Related to Carotid Endarterectomy.

OBJECTIVE: To explore the methods of protecting the external branch of the superior laryngeal nerve during carotid endarterectomy through microsurgical anatomic study of the external branch of the superior laryngeal nerve in cadaveric specimens. METHODS: A total of 30 cadaveric specimens (60 sides) were dissected to measure the thickness of the external branch of the superior laryngeal nerve. A triangular area was exposed, bounded by the lower border of the digastric muscle superiorly, the medial edge of the sternocleidomastoid muscle laterally, and the upper border of the superior thyroid artery inferiorly. The probability of the occurrence of the external branch of the superior laryngeal nerve in this area was observed and recorded. The distance among the midpoint of the external branch of the superior laryngeal nerve in this area with the tip of the mastoid process and the angle of the mandible as well as the bifurcation of the common carotid artery was measured and recorded. RESULTS: Among 30 specimens of cadaveric heads (60 sides) examined 53 external branches of the superior laryngeal nerve were observed while 7 were absent. Of the 53 branches observed, 5 were located outside the anatomic triangle region mentioned above, while the remaining 48 branches were located within the anatomic triangle region with a probability of ~80%. The thickness of the midpoint of the external branches of the superior laryngeal nerve within the anatomic triangle region was 0.93 mm (0.72-1.15 mm [±0.83 SD]), located 0.34 cm [-1.62-2.43 cm (±0.96 SD)] posterior to the angle of the mandible, 1.28 cm (-1.33 to 3.42 cm (±0.93 SD)] inferiorly; 2.84 cm (0.51-5.14 cm±1.09 SD) anterior to the tip of the mastoid process, 4.51 cm (2.82-6.39 cm±0.76 SD) inferiorly; 1.64 cm [0.57-3.78 cm (±0.89 SD)] superior to the bifurcation of the carotid artery. CONCLUSIONS: During carotid endarterectomy procedure, using the cervical anatomic triangle region, as well as the angle of the mandible, the tip of the mastoid process, and the bifurcation of the carotid artery as anatomic landmarks, is of significant clinical importance for protecting the external branches of the superior laryngeal nerve.

CAM-VT: A Weakly supervised cervical cancer nest image identification approach using conjugated attention mechanism and visual transformer.

Cervical cancer is the fourth most common cancer among women, and cytopathological images are often used to screen for this cancer. However, manual examination is very troublesome and the misdiagnosis rate is high. In addition, cervical cancer nest cells are denser and more complex, with high overlap and opacity, increasing the difficulty of identification. The appearance of the computer aided automatic diagnosis system solves this problem. In this paper, a weakly supervised cervical cancer nest image identification approach using Conjugated Attention Mechanism and Visual Transformer (CAM-VT), which can analyze pap slides quickly and accurately. CAM-VT proposes conjugated attention mechanism and visual transformer modules for local and global feature extraction respectively, and then designs an ensemble learning module to further improve the identification capability. In order to determine a reasonable interpretation, comparative experiments are conducted on our datasets. The average accuracy of the validation set of three repeated experiments using CAM-VT framework is 88.92%, which is higher than the optimal result of 22 well-known deep learning models. Moreover, we conduct ablation experiments and extended experiments on Hematoxylin and Eosin stained gastric histopathological image datasets to verify the ability and generalization ability of the framework. Finally, the top 5 and top 10 positive probability values of cervical nests are 97.36% and 96.84%, which have important clinical and practical significance. The experimental results show that the proposed CAM-VT framework has excellent performance in potential cervical cancer nest image identification tasks for practical clinical work.

Is the aspect ratio of cells important in deep learning? A robust comparison of deep learning methods for multi-scale cytopathology cell image classification: From convolutional neural networks to visual transformers.

Cervical cancer is a very common and fatal type of cancer in women. Cytopathology images are often used to screen for this cancer. Given that there is a possibility that many errors can occur during manual screening, a computer-aided diagnosis system based on deep learning has been developed. Deep learning methods require a fixed dimension of input images, but the dimensions of clinical medical images are inconsistent. The aspect ratios of the images suffer while resizing them directly. Clinically, the aspect ratios of cells inside cytopathological images provide important information for doctors to diagnose cancer. Therefore, it is difficult to resize directly. However, many existing studies have resized the images directly and have obtained highly robust classification results. To determine a reasonable interpretation, we have conducted a series of comparative experiments. First, the raw data of the SIPaKMeD dataset are pre-processed to obtain standard and scaled datasets. Then, the datasets are resized to 224 × 224 pixels. Finally, 22 deep learning models are used to classify the standard and scaled datasets. The results of the study indicate that deep learning models are robust to changes in the aspect ratio of cells in cervical cytopathological images. This conclusion is also validated via the Herlev dataset.

DeepCervix: A deep learning-based framework for the classification of cervical cells using hybrid deep feature fusion techniques.

Cervical cancer, one of the most common fatal cancers among women, can be prevented by regular screening to detect any precancerous lesions at early stages and treat them. Pap smear test is a widely performed screening technique for early detection of cervical cancer, whereas this manual screening method suffers from high false-positive results because of human errors. To improve the manual screening practice, machine learning (ML) and deep learning (DL) based computer-aided diagnostic (CAD) systems have been investigated widely to classify cervical Pap cells. Most of the existing studies require pre-segmented images to obtain good classification results. In contrast, accurate cervical cell segmentation is challenging because of cell clustering. Some studies rely on handcrafted features, which cannot guarantee the classification stage's optimality. Moreover, DL provides poor performance for a multiclass classification task when there is an uneven distribution of data, which is prevalent in the cervical cell dataset. This investigation has addressed those limitations by proposing DeepCervix, a hybrid deep feature fusion (HDFF) technique based on DL, to classify the cervical cells accurately. Our proposed method uses various DL models to capture more potential information to enhance classification performance. Our proposed HDFF method is tested on the publicly available SIPaKMeD dataset and compared the performance with base DL models and the late fusion (LF) method. For the SIPaKMeD dataset, we have obtained the state-of-the-art classification accuracy of 99.85%, 99.38%, and 99.14% for 2-class, 3-class, and 5-class classification. This method is also tested on the Herlev dataset and achieves an accuracy of 98.32% for 2-class and 90.32% for 7-class classification. The source code of the DeepCervix model is available at: https://github.com/Mamunur-20/DeepCervix.