An objective validation of polyp and instrument segmentation methods in colonoscopy through Medico 2020 polyp segmentation and MedAI 2021 transparency challenges

Automatic analysis of colonoscopy images has been an active field of research motivated by the importance of early detection of precancerous polyps. However, detecting polyps during the live examination can be challenging due to various factors such as variation of skills and experience among the endoscopists, lack of attentiveness, and fatigue leading to a high polyp miss-rate. Deep learning has emerged as a promising solution to this challenge as it can assist endoscopists in detecting and classifying overlooked polyps and abnormalities in real time. In addition to the algorithm's accuracy, transparency and interpretability are crucial to explaining the whys and hows of the algorithm's prediction. Further, most algorithms are developed in private data, closed source, or proprietary software, and methods lack reproducibility. Therefore, to promote the development of efficient and transparent methods, we have organized the "Medico automatic polyp segmentation (Medico 2020)" and "MedAI: Transparency in Medical Image Segmentation (MedAI 2021)" competitions. We present a comprehensive summary and analyze each contribution, highlight the strength of the best-performing methods, and discuss the possibility of clinical translations of such methods into the clinic. For the transparency task, a multi-disciplinary team, including expert gastroenterologists, accessed each submission and evaluated the team based on open-source practices, failure case analysis, ablation studies, usability and understandability of evaluations to gain a deeper understanding of the models' credibility for clinical deployment. Through the comprehensive analysis of the challenge, we not only highlight the advancements in polyp and surgical instrument segmentation but also encourage qualitative evaluation for building more transparent and understandable AI-based colonoscopy systems

Evaluation Of Maternal Patient Experience During Covid-19 Using Natural Language Processing

Healthcare policymakers are constantly investigating how to improve this situation and provide a more patient-centered care. Delivering excellent medical care involves ensuring that patients have a positive experience. Most healthcare organizations use patient survey feedback, like HCAHPS, to measure their patients\u27 experiences. The United States has the highest maternal mortality or morbidity rate of the developed countries, so we used maternal patients as the patient cohort to evaluate various touchpoints. The power of social media can be harnessed to provide researchers with valuable insights into understanding patient\u27s experience and care. We used the COVID-19Tweets Dataset, which has over twenty-eight million tweets, to evaluate patient experience using Natural Language Processing (NLP) and extract tweets from the US with words relevant to maternal patients. This research\u27s objective is to develop a model to evaluate the patient experience during the COVID-19 pandemic. We created word clouds, word clustering, frequency analysis, and network analysis of words that relate to “pains” and “gains” expressed through social media regarding the maternal patient experience. This model will help process improvement experts without domain expertise to efficiently understand various challenges in the domain. Such insights can help decision-makers improve the patient care system. Additionally, the model will also discover if there is any racial health inequity faced by any particular group. Artificial Intelligence can be used to get information from social media about how patients feel. This allows healthcare organizations to be more patient centered

Evaluation of Maternal Patient Experience During COVID-19 Using Natural Language Processing

Healthcare policymakers are constantly investigating how to improve this situation and provide a more patient-centered care. Delivering excellent medical care involves ensuring that patients have a positive experience. Most healthcare organizations use patient survey feedback, like HCAHPS, to measure their patients\u27 experiences. The United States has the highest maternal mortality or morbidity rate of the developed countries, so we used maternal patients as the patient cohort to evaluate various touchpoints. The power of social media can be harnessed to provide researchers with valuable insights into understanding patient\u27s experience and care. We used the COVID-19Tweets Dataset, which has over twenty-eight million tweets, to evaluate patient experience using Natural Language Processing (NLP) and extract tweets from the US with words relevant to maternal patients. This research\u27s objective is to develop a model to evaluate the patient experience during the COVID-19 pandemic. We created word clouds, word clustering, frequency analysis, and network analysis of words that relate to “pains” and “gains” expressed through social media regarding the maternal patient experience. This model will help process improvement experts without domain expertise to efficiently understand various challenges in the domain. Such insights can help decision-makers improve the patient care system. Additionally, the model will also discover if there is any racial health inequity faced by any particular group. Artificial Intelligence can be used to get information from social media about how patients feel. This allows healthcare organizations to be more patient centered

EM-Net: An Efficient M-Net for segmentation of surgical instruments in colonoscopy frames

This paper addresses the Instrument Segmentation Task, a subtask for the “MedAI: Transparency in Medical Image Segmentation” challenge. To accomplish the subtask, our team “Med_Seg_JU” has proposed a deep learning-based framework, namely “EM-Net: An Efficient M-Net for segmentation of surgical instruments in colonoscopy frames”. The proposed framework is inspired by the M-Net architecture. In this architecture, we have incorporated the EfficientNet B3 module with U-Net as the backbone. Our proposed method obtained a JC of 0.8205, DSC of 0.8632, PRE of 0.8464, REC of 0.9005, F1 of 0.8632, and ACC of 0.9799 as evaluated by the challenge organizers on a separate test dataset. These results justify the efficacy of our proposed method in the segmentation of the surgical instruments.This paper addresses the Instrument Segmentation Task, a subtask for the “MedAI: Transparency in Medical Image Segmentation” challenge. To accomplish the subtask, our team “Med_Seg_JU” has proposed a deep learning-based framework,namely “EM-Net: An Efficient M-Net for segmentation of surgical instruments in colonoscopy frames”. The proposedframework is inspired by the M-Net architecture. In this architecture, we have incorporated the EfficientNet B3 module withU-Net as the backbone. Our proposed method obtained a JC of 0.8205, DSC of 0.8632, PRE of 0.8464, REC of 0.9005, F1of 0.8632, and ACC of 0.9799 as evaluated by the challenge organizers on a separate test dataset. These results justify theefficacy of our proposed method in the segmentation of the surgical instruments