Yanjinlkham Chuluunbaatar, Saakshi Bansal, Andrew Brodie, Anand Sharma, Nikhil Vasdev
Testicular cancer is often overshadowed by other cancers despite being the most common cancer in men aged 15 to 34 years. This systematic review focuses on the potential of machine learning and deep learning techniques in the areas of testicular cancer imaging and histopathology, where artificial intelligence (AI) could assist in diagnosis, evaluation, and prognostication. Various studies have highlighted AI’s ability to accurately distinguish between benign and malignant lesions and characterisation within malignant lesions using magnetic resonance imaging (MRI) radiomics. Models have also been used in predicting histopathological findings to allow for greater accuracy and reproducibility. Further work is required to explore AI implementation in ultrasound imaging, which is the cheapest and most used modality.
{"title":"The current use of artificial intelligence in testicular cancer: a systematic review","authors":"Yanjinlkham Chuluunbaatar, Saakshi Bansal, Andrew Brodie, Anand Sharma, Nikhil Vasdev","doi":"10.20517/ais.2023.26","DOIUrl":"https://doi.org/10.20517/ais.2023.26","url":null,"abstract":"Testicular cancer is often overshadowed by other cancers despite being the most common cancer in men aged 15 to 34 years. This systematic review focuses on the potential of machine learning and deep learning techniques in the areas of testicular cancer imaging and histopathology, where artificial intelligence (AI) could assist in diagnosis, evaluation, and prognostication. Various studies have highlighted AI’s ability to accurately distinguish between benign and malignant lesions and characterisation within malignant lesions using magnetic resonance imaging (MRI) radiomics. Models have also been used in predicting histopathological findings to allow for greater accuracy and reproducibility. Further work is required to explore AI implementation in ultrasound imaging, which is the cheapest and most used modality.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135944500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The medical technological revolution has transformed the nature with which we deliver care. Adjuncts such as artificial intelligence and machine learning have underpinned this. The applications to the field of endoscopy are numerous. Malignant polyps represent a significant diagnostic dilemma as they lie in an area in which mischaracterisation may mean the difference between an endoscopic procedure and a formal bowel resection. This has implications for patients’ oncological outcomes, morbidity and mortality, especially if post-procedure histopathology upstages disease. We have made significant strides with the applications of artificial intelligence to colonoscopic detection. Deep learning algorithms are able to be created from video and image databases. These have been applied to traditional, human-derived, classification methods, such as Paris or Kudo, with up to 93% accuracy. Furthermore, multimodal characterisation systems have been developed, which also factor in patient demographics and colonic location to provide an estimation of invasion and endoscopic resectability with over 90% accuracy. Although the technology is still evolving, and the lack of high-quality randomised controlled trials limits clinical usability, there is an exciting horizon upon us for artificial intelligence-augmented endoscopy.
{"title":"AI in colonoscopy - detection and characterisation of malignant polyps","authors":"Taner Shakir, Rawen Kader, Chetan Bhan, Manish Chand","doi":"10.20517/ais.2023.17","DOIUrl":"https://doi.org/10.20517/ais.2023.17","url":null,"abstract":"The medical technological revolution has transformed the nature with which we deliver care. Adjuncts such as artificial intelligence and machine learning have underpinned this. The applications to the field of endoscopy are numerous. Malignant polyps represent a significant diagnostic dilemma as they lie in an area in which mischaracterisation may mean the difference between an endoscopic procedure and a formal bowel resection. This has implications for patients’ oncological outcomes, morbidity and mortality, especially if post-procedure histopathology upstages disease. We have made significant strides with the applications of artificial intelligence to colonoscopic detection. Deep learning algorithms are able to be created from video and image databases. These have been applied to traditional, human-derived, classification methods, such as Paris or Kudo, with up to 93% accuracy. Furthermore, multimodal characterisation systems have been developed, which also factor in patient demographics and colonic location to provide an estimation of invasion and endoscopic resectability with over 90% accuracy. Although the technology is still evolving, and the lack of high-quality randomised controlled trials limits clinical usability, there is an exciting horizon upon us for artificial intelligence-augmented endoscopy.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136129668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Andrew A. Gumbs, Roland Croner, Mohammed Abu-Hilal, Elisa Bannone, Takeaki Ishizawa, Gaya Spolverato, Isabella Frigerio, Ajith Siriwardena, Nouredin Messaoudi
The journal Artificial Intelligence Surgery was established to explore the integration of Artificial Intelligence (AI) in surgery. It originated from the desire to understand the potential of true robotic surgery, as existing robotic systems are tele-manipulators rather than autonomous robots. AI’s role in surgery involves levels of autonomy and a balance between human expertise and technological advancements. In this regard, a new field of Surgiomics emerges, integrating patient data such as genomics, radiomics, and pathomics to enhance surgical decision-making. Overcoming limitations in surgical data analysis, AI processes vast amounts of data, detects subtle patterns, and explores complex relationships. As Surgiomics continues to evolve, it holds the potential to reshape surgical patient management. Initiatives like the Artificial intelligence, Radiomics, Genomics, Oncopathomics and Surgomics (AiRGOS) Project aim to develop AI algorithms for precision therapeutic treatments in cancer patients using radiologic imaging, genomic sequencing, and clinical data. In this commentary, we envision a future where AI technologies revolutionize surgical decision-making and create personalized treatment plans based on comprehensive patient data.
{"title":"Surgomics and the Artificial intelligence, Radiomics, Genomics, Oncopathomics and Surgomics (AiRGOS) Project","authors":"Andrew A. Gumbs, Roland Croner, Mohammed Abu-Hilal, Elisa Bannone, Takeaki Ishizawa, Gaya Spolverato, Isabella Frigerio, Ajith Siriwardena, Nouredin Messaoudi","doi":"10.20517/ais.2023.24","DOIUrl":"https://doi.org/10.20517/ais.2023.24","url":null,"abstract":"The journal Artificial Intelligence Surgery was established to explore the integration of Artificial Intelligence (AI) in surgery. It originated from the desire to understand the potential of true robotic surgery, as existing robotic systems are tele-manipulators rather than autonomous robots. AI’s role in surgery involves levels of autonomy and a balance between human expertise and technological advancements. In this regard, a new field of Surgiomics emerges, integrating patient data such as genomics, radiomics, and pathomics to enhance surgical decision-making. Overcoming limitations in surgical data analysis, AI processes vast amounts of data, detects subtle patterns, and explores complex relationships. As Surgiomics continues to evolve, it holds the potential to reshape surgical patient management. Initiatives like the Artificial intelligence, Radiomics, Genomics, Oncopathomics and Surgomics (AiRGOS) Project aim to develop AI algorithms for precision therapeutic treatments in cancer patients using radiologic imaging, genomic sequencing, and clinical data. In this commentary, we envision a future where AI technologies revolutionize surgical decision-making and create personalized treatment plans based on comprehensive patient data.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135010919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Riccardo De Robertis, Marco Todesco, Daniele Autelitano, Flavio Spoto, Mirko D’Onofrio
Radiomics is an advanced computational analysis of biomedical images that aims to obtain a detailed, objective, and multidimensional characterization of biological tissues. Radiomics features ultimately represent the physiopathology of the tissue under study and can be used to characterize and quantify the spatial distribution and interactions between the voxels that compose a biomedical image. The aim of this paper was to review the current role of radiomics in hepato-bilio-pancreatic surgery by analyzing systematic reviews, meta-analyses and the most relevant published series. Literature data revealed that radiomics is a promising tool in improving the non-invasive characterization and preoperative staging of hepato-bilio-pancreatic neoplasms. Nevertheless, there are major limitations in this approach, mainly linked to the lack of standardization in image acquisition, that result in a significant translational gap between research and clinical practice.
{"title":"The role of radiomics in hepato-bilio-pancreatic surgery: a literature review","authors":"Riccardo De Robertis, Marco Todesco, Daniele Autelitano, Flavio Spoto, Mirko D’Onofrio","doi":"10.20517/ais.2023.18","DOIUrl":"https://doi.org/10.20517/ais.2023.18","url":null,"abstract":"Radiomics is an advanced computational analysis of biomedical images that aims to obtain a detailed, objective, and multidimensional characterization of biological tissues. Radiomics features ultimately represent the physiopathology of the tissue under study and can be used to characterize and quantify the spatial distribution and interactions between the voxels that compose a biomedical image. The aim of this paper was to review the current role of radiomics in hepato-bilio-pancreatic surgery by analyzing systematic reviews, meta-analyses and the most relevant published series. Literature data revealed that radiomics is a promising tool in improving the non-invasive characterization and preoperative staging of hepato-bilio-pancreatic neoplasms. Nevertheless, there are major limitations in this approach, mainly linked to the lack of standardization in image acquisition, that result in a significant translational gap between research and clinical practice.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135981962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The 19th annual meeting of Egyptian Society of Laparoscopic Surgeons (ESLS) congress report","authors":"H. Taher, Faheem Bassiony, H. Shaker","doi":"10.20517/ais.2023.11","DOIUrl":"https://doi.org/10.20517/ais.2023.11","url":null,"abstract":"","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67656916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mucinous cysts of the pancreas represent the most common identifiable precursor to pancreatic cancer. Evidence-based guidelines for screening and surveillance exist, but many patients are either not properly identified or lost to follow-up. Artificial Intelligence, specifically computational linguistics models, can dramatically improve patient identification and mitigate risk through modernizing pancreatic cyst longitudinal surveillance. Herein we discuss the risk associated with mucinous cysts of the pancreas and modern approaches to patient identification and follow-up.
{"title":"Role of artificial intelligence in pancreatic cystic neoplasms: modernizing the identification and longitudinal management of pancreatic cysts","authors":"R. Langan, H. Pitt, E. Schneider","doi":"10.20517/ais.2023.13","DOIUrl":"https://doi.org/10.20517/ais.2023.13","url":null,"abstract":"Mucinous cysts of the pancreas represent the most common identifiable precursor to pancreatic cancer. Evidence-based guidelines for screening and surveillance exist, but many patients are either not properly identified or lost to follow-up. Artificial Intelligence, specifically computational linguistics models, can dramatically improve patient identification and mitigate risk through modernizing pancreatic cyst longitudinal surveillance. Herein we discuss the risk associated with mucinous cysts of the pancreas and modern approaches to patient identification and follow-up.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67656972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Flora Wen Xin Xu, Sarah S. Tang, Hann Natalie Soh, N. Pang, G. Bonney
Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer-related death worldwide and prognosis remains poor. The recent paradigm shifts in management algorithms of such patients have resulted in unique challenges in the early identification of HCC, prognosis, surgical outcomes, prioritization of potential transplant recipients, donor-recipient matching, and so on. In recent years, advancements in artificial intelligence (AI) capabilities have shown potential in HCC treatment. In this narrative review, we outline first the different types of AI models that are applied in clinical practice and then focus on the frontiers of AI research in the diagnosis, prognostication, and treatment of HCC, particularly in classification of indeterminate liver lesions, tumor staging, survival prediction, improving equity in transplant recipient selection, prediction of treatment response and prognosis. We show that US coupled with AI-driven predictive models can provide accurate noninvasive screening tools for early disease. While AI models applied to contrast-enhanced CT, MRI and PET studies may appear to have limited clinical utility in disease diagnosis and differentials, owing to their accuracy, we highlighted the importance of such models in predicting pathological findings preoperatively. Despite the availability of many accurate, sensitive, and specific AI algorithms that outperform traditional scoring systems, they have not been widely used in clinical practice. The challenges in AI application, including distributional shift and imbalanced data, lack of standardization, and the ‘black box’ phenomenon, are addressed here. The importance of AI in the future of HCC makes it important for clinicians to have a good understanding of different AI techniques, their benefits, and potential pitfalls.
{"title":"Augmenting care in hepatocellular carcinoma with artificial intelligence","authors":"Flora Wen Xin Xu, Sarah S. Tang, Hann Natalie Soh, N. Pang, G. Bonney","doi":"10.20517/ais.2022.33","DOIUrl":"https://doi.org/10.20517/ais.2022.33","url":null,"abstract":"Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer-related death worldwide and prognosis remains poor. The recent paradigm shifts in management algorithms of such patients have resulted in unique challenges in the early identification of HCC, prognosis, surgical outcomes, prioritization of potential transplant recipients, donor-recipient matching, and so on. In recent years, advancements in artificial intelligence (AI) capabilities have shown potential in HCC treatment. In this narrative review, we outline first the different types of AI models that are applied in clinical practice and then focus on the frontiers of AI research in the diagnosis, prognostication, and treatment of HCC, particularly in classification of indeterminate liver lesions, tumor staging, survival prediction, improving equity in transplant recipient selection, prediction of treatment response and prognosis. We show that US coupled with AI-driven predictive models can provide accurate noninvasive screening tools for early disease. While AI models applied to contrast-enhanced CT, MRI and PET studies may appear to have limited clinical utility in disease diagnosis and differentials, owing to their accuracy, we highlighted the importance of such models in predicting pathological findings preoperatively. Despite the availability of many accurate, sensitive, and specific AI algorithms that outperform traditional scoring systems, they have not been widely used in clinical practice. The challenges in AI application, including distributional shift and imbalanced data, lack of standardization, and the ‘black box’ phenomenon, are addressed here. The importance of AI in the future of HCC makes it important for clinicians to have a good understanding of different AI techniques, their benefits, and potential pitfalls.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67656464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The field of totally endoscopic, robotic-assisted mitral valve surgery has progressively gained popularity over the last twenty-five years. In this narrative review, we sought to discuss this expanding field from a historical perspective, a technical perspective, and a training perspective.
{"title":"State of the art and new frontiers in robotic mitral valve surgery","authors":"A. Amabile, S. Ragnarsson, M. Krane, A. Geirsson","doi":"10.20517/ais.2023.10","DOIUrl":"https://doi.org/10.20517/ais.2023.10","url":null,"abstract":"The field of totally endoscopic, robotic-assisted mitral valve surgery has progressively gained popularity over the last twenty-five years. In this narrative review, we sought to discuss this expanding field from a historical perspective, a technical perspective, and a training perspective.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67656671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Saakshi Bansal, Yanjinlkham Chuluunbaatar, A. Brodie, N. Vasdev
The advancement of computational abilities has taken us from the days of machines performing simple, one-dimensional tasks to themselves learning and applying knowns to unknowns. Artificial intelligence (AI) has become integral in daily life, yet there is vast room for application in surgery. Cancer research can divert attention from more prevalent benign diseases which may equally cause a significant impact on quality of life. Here we review recent advancements in the field of AI for diagnostics, management, and prognostication of benign prostatic hyperplasia, evaluating the strengths and limitations of these approaches with implications for future research.
{"title":"Applications of artificial intelligence in benign prostatic hyperplasia","authors":"Saakshi Bansal, Yanjinlkham Chuluunbaatar, A. Brodie, N. Vasdev","doi":"10.20517/ais.2023.07","DOIUrl":"https://doi.org/10.20517/ais.2023.07","url":null,"abstract":"The advancement of computational abilities has taken us from the days of machines performing simple, one-dimensional tasks to themselves learning and applying knowns to unknowns. Artificial intelligence (AI) has become integral in daily life, yet there is vast room for application in surgery. Cancer research can divert attention from more prevalent benign diseases which may equally cause a significant impact on quality of life. Here we review recent advancements in the field of AI for diagnostics, management, and prognostication of benign prostatic hyperplasia, evaluating the strengths and limitations of these approaches with implications for future research.","PeriodicalId":72305,"journal":{"name":"Artificial intelligence surgery","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67656663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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