Pub Date : 2019-05-01DOI: 10.21037/JMAI.2019.04.02
Kendall J. Kiser, C. Fuller, V. Reed
Among medical specialties, radiation oncology has long been an innovator and early adopter of therapeutic technologies. This specialty is now situated in prime position to be revolutionized by advances in artificial intelligence (AI), especially machine and deep learning. AI has been investigated by radiation oncologists and physicists in both general and niche radiotherapy planning tasks and has often demonstrated performance that is indistinguishable from human experts, while substantially shortening the time required to complete these tasks. We sought to review applications of AI to domains germane to radiation oncology, namely: image segmentation, treatment plan generation and optimization, normal tissue complication probability modeling, quality assurance (QA), and adaptive re-planning. We sought likewise to consider obstacles to AI adoption in the radiotherapy clinic, now primarily political, legal, and ethical rather than technical in nature.
{"title":"Artificial intelligence in radiation oncology treatment planning: a brief overview","authors":"Kendall J. Kiser, C. Fuller, V. Reed","doi":"10.21037/JMAI.2019.04.02","DOIUrl":"https://doi.org/10.21037/JMAI.2019.04.02","url":null,"abstract":"Among medical specialties, radiation oncology has long been an innovator and early adopter of therapeutic technologies. This specialty is now situated in prime position to be revolutionized by advances in artificial intelligence (AI), especially machine and deep learning. AI has been investigated by radiation oncologists and physicists in both general and niche radiotherapy planning tasks and has often demonstrated performance that is indistinguishable from human experts, while substantially shortening the time required to complete these tasks. We sought to review applications of AI to domains germane to radiation oncology, namely: image segmentation, treatment plan generation and optimization, normal tissue complication probability modeling, quality assurance (QA), and adaptive re-planning. We sought likewise to consider obstacles to AI adoption in the radiotherapy clinic, now primarily political, legal, and ethical rather than technical in nature.","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.21037/JMAI.2019.04.02","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45894472","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}
Pub Date : 2019-04-19DOI: 10.21037/JMAI.2019.04.01
Jianrong Wu, Tianyi Qian
Lung cancer is the top cause for deaths by cancers whose 5-year survival rate is less than 20%. To improve the survival rate of patients with lung cancers, the early detection and early diagnosis is significant. Furthermore, early detection of pulmonary nodules is essential for the detection and diagnosis of lung cancer in early stage. The National Lung Screening Trial (NLST) showed annual screening by low-dose computed tomography (LDCT) could help to reduce the deaths caused by lung cancer of high-risk subjects by 20% comparing with screening by chest radiography. In past decade, there has been lots of works on computer-aided detection (CADe) and computer-aided diagnosis (CADx) for pulmonary nodules in computed tomography (CT) scans, whose target is to detect, segment the nodules and further classify them into benign and malignant efficiently and precisely. This survey reviews some recent works on detection, segmentation and classification for pulmonary nodule in CT scans with deep learning techniques.
{"title":"A survey of pulmonary nodule detection, segmentation and classification in computed tomography with deep learning techniques","authors":"Jianrong Wu, Tianyi Qian","doi":"10.21037/JMAI.2019.04.01","DOIUrl":"https://doi.org/10.21037/JMAI.2019.04.01","url":null,"abstract":"Lung cancer is the top cause for deaths by cancers whose 5-year survival rate is less than 20%. To improve the survival rate of patients with lung cancers, the early detection and early diagnosis is significant. Furthermore, early detection of pulmonary nodules is essential for the detection and diagnosis of lung cancer in early stage. The National Lung Screening Trial (NLST) showed annual screening by low-dose computed tomography (LDCT) could help to reduce the deaths caused by lung cancer of high-risk subjects by 20% comparing with screening by chest radiography. In past decade, there has been lots of works on computer-aided detection (CADe) and computer-aided diagnosis (CADx) for pulmonary nodules in computed tomography (CT) scans, whose target is to detect, segment the nodules and further classify them into benign and malignant efficiently and precisely. This survey reviews some recent works on detection, segmentation and classification for pulmonary nodule in CT scans with deep learning techniques.","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.21037/JMAI.2019.04.01","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46533040","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}
Pub Date : 2019-03-19DOI: 10.21037/JMAI.2019.03.02
A. Tarnawski, A. Ahluwalia
Artificial intelligence (AI), also referred to as machine intelligence, has been increasingly entering all avenues of our lives (1-5). AI has enabled facial, object, speech, gesture and writing recognition, language translation, autonomous cars, internet searches, cyber and home security and many other areas. It has revolutionized diverse aspects of medical care, including electronic health records, guidance in medical diagnosis and treatment decisions, medical statistics, analysis of X-rays, CT-scans, MRIs, electrocardiograms (EKGs), evaluation of endoscopic and histologic images, robotics, and cellular and molecular biology including arrays and genome-, proteome- and metabolome- “omics”.
{"title":"Application of artificial intelligence for the assessment of mucosal healing and inflammation","authors":"A. Tarnawski, A. Ahluwalia","doi":"10.21037/JMAI.2019.03.02","DOIUrl":"https://doi.org/10.21037/JMAI.2019.03.02","url":null,"abstract":"Artificial intelligence (AI), also referred to as machine intelligence, has been increasingly entering all avenues of our lives (1-5). AI has enabled facial, object, speech, gesture and writing recognition, language translation, autonomous cars, internet searches, cyber and home security and many other areas. It has revolutionized diverse aspects of medical care, including electronic health records, guidance in medical diagnosis and treatment decisions, medical statistics, analysis of X-rays, CT-scans, MRIs, electrocardiograms (EKGs), evaluation of endoscopic and histologic images, robotics, and cellular and molecular biology including arrays and genome-, proteome- and metabolome- “omics”.","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.21037/JMAI.2019.03.02","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46177868","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}
Pub Date : 2019-03-15DOI: 10.21037/JMAI.2019.03.03
A. Retico, M. Fantacci
The efficient detection of lung nodules is an extremely important and challenging task, which has required in the recent years a joint effort by a wide community of scientists including chest doctors, radiologists, nuclear medicine physicians, and experts in medical instrumentation, image processing and artificial intelligence.
{"title":"The potential contribution of artificial intelligence to dose reduction in diagnostic imaging of lung cancer","authors":"A. Retico, M. Fantacci","doi":"10.21037/JMAI.2019.03.03","DOIUrl":"https://doi.org/10.21037/JMAI.2019.03.03","url":null,"abstract":"The efficient detection of lung nodules is an extremely important and challenging task, which has required in the recent years a joint effort by a wide community of scientists including chest doctors, radiologists, nuclear medicine physicians, and experts in medical instrumentation, image processing and artificial intelligence.","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.21037/JMAI.2019.03.03","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45212058","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}
Pub Date : 2019-03-14DOI: 10.21037/JMAI.2019.03.01
R. Yeung, E. Capobianco
A lot has been already said about the emerging role of big data in health and disease. Large scale data efforts are increasingly being undertaken in response to the advent of Personalized and Precision Medicine and in association with both the “omics revolution” and the Electronic Health Records centrality. big data have demonstrated that their complex characteristics bring both strength factors and bottlenecks to research problems widely identified, analyzed and reviewed across many sectors of medicine and public health. As the most significant feature of big data is “variety”, and this implies heterogeneity, our knowledge in complex disease contexts may substantially benefit from the fusion of different data types when a major role is assigned to harmonization and interoperability strategies. We discuss of an example, diabetes.
{"title":"Big data in health and disease: re-processing information for discovery and validation","authors":"R. Yeung, E. Capobianco","doi":"10.21037/JMAI.2019.03.01","DOIUrl":"https://doi.org/10.21037/JMAI.2019.03.01","url":null,"abstract":"A lot has been already said about the emerging role of big data in health and disease. Large scale data efforts are increasingly being undertaken in response to the advent of Personalized and Precision Medicine and in association with both the “omics revolution” and the Electronic Health Records centrality. big data have demonstrated that their complex characteristics bring both strength factors and bottlenecks to research problems widely identified, analyzed and reviewed across many sectors of medicine and public health. As the most significant feature of big data is “variety”, and this implies heterogeneity, our knowledge in complex disease contexts may substantially benefit from the fusion of different data types when a major role is assigned to harmonization and interoperability strategies. We discuss of an example, diabetes.","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.21037/JMAI.2019.03.01","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48979944","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}
Pub Date : 2019-03-07DOI: 10.21037/JMAI.2019.02.02
Aasa Shimizu, Kenjiro Sawada, T. Kimura
Ovarian cancer is also known as the “silent killer” because this type of cancer spreads widely without the occurrence of any symptoms (1). Currently, ovarian cancer accounts for approximately 5% cancer deaths among women in the United States (2). Worldwide statistics from 185 countries indicated 295,414 new cancer cases and 184,799 deaths from this disease in 2018 (3). High-grade serous carcinoma is the most common histological type accounting for the majority of advanced ovarian cancers.
{"title":"Development of novel approaches to detect ovarian cancer recurrence","authors":"Aasa Shimizu, Kenjiro Sawada, T. Kimura","doi":"10.21037/JMAI.2019.02.02","DOIUrl":"https://doi.org/10.21037/JMAI.2019.02.02","url":null,"abstract":"Ovarian cancer is also known as the “silent killer” because this type of cancer spreads widely without the occurrence of any symptoms (1). Currently, ovarian cancer accounts for approximately 5% cancer deaths among women in the United States (2). Worldwide statistics from 185 countries indicated 295,414 new cancer cases and 184,799 deaths from this disease in 2018 (3). High-grade serous carcinoma is the most common histological type accounting for the majority of advanced ovarian cancers.","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.21037/JMAI.2019.02.02","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41487875","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}
Pub Date : 2019-02-07DOI: 10.21037/jmai.2019.06.01
Z. Hoodbhoy, B. Hasan, Khan Siddiqui
Artificial intelligence (AI) has revolutionized healthcare in the past few decades (1). Initially being used solely as a medical decision support system, it is anticipated that AI has a potential role in personalized medicine, patient monitoring and improve health service delivery and management (2). AI has penetrated the health care domain rapidly in high income settings where an estimated USD 150 million could be saved with such applications in the next 5 years (3). However, there is limited literature available on its use in low resource settings.
{"title":"Does artificial intelligence have any role in healthcare in low resource settings?","authors":"Z. Hoodbhoy, B. Hasan, Khan Siddiqui","doi":"10.21037/jmai.2019.06.01","DOIUrl":"https://doi.org/10.21037/jmai.2019.06.01","url":null,"abstract":"Artificial intelligence (AI) has revolutionized healthcare in the past few decades (1). Initially being used solely as a medical decision support system, it is anticipated that AI has a potential role in personalized medicine, patient monitoring and improve health service delivery and management (2). AI has penetrated the health care domain rapidly in high income settings where an estimated USD 150 million could be saved with such applications in the next 5 years (3). However, there is limited literature available on its use in low resource settings.","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.21037/jmai.2019.06.01","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47443545","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}
Pub Date : 2019-01-03DOI: 10.21037/jmai.2019.02.01
Peerawut Deeprasertkul, C. Dailing, M. Budoff
Obstructive coronary artery disease (CAD) is the leading cause of death in the United States (1). Increasing in awareness, and management has decreased the mortality tremendously (1). Cardiac stress test has been widely used to detect the obstructive CAD, in patients with symptom of cardiac angina. However, due to limited sensitivity, time consuming nature of stress imaging, and the expense, cardiac stress testing is not an ideal test for diagnosing the etiology of chest pain.
{"title":"Deep learning analysis of the myocardium in coronary computerized tomography angiography for identification significant coronary artery stenosis","authors":"Peerawut Deeprasertkul, C. Dailing, M. Budoff","doi":"10.21037/jmai.2019.02.01","DOIUrl":"https://doi.org/10.21037/jmai.2019.02.01","url":null,"abstract":"Obstructive coronary artery disease (CAD) is the leading cause of death in the United States (1). Increasing in awareness, and management has decreased the mortality tremendously (1). Cardiac stress test has been widely used to detect the obstructive CAD, in patients with symptom of cardiac angina. However, due to limited sensitivity, time consuming nature of stress imaging, and the expense, cardiac stress testing is not an ideal test for diagnosing the etiology of chest pain.","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.21037/jmai.2019.02.01","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48896440","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}
Pub Date : 2019-01-01DOI: 10.21037/JMAI.2019.01.01
C. Perone, J. Cohen-Adad
It is not a secret that recent advances in deep learning (1) methods have achieved a scientific and engineering milestone in many different fields such as natural language processing, computer vision, speech recognition, object detection, and segmentation, to name a few. Different applications of deep learning to medical imaging started to appear first in workshops, conferences and then in journals. According to a recent survey (2), the number of papers grew rapidly in 2015 and 2016. Nowadays, deep learning methods are pervasive throughout the entire medical imaging community, with Convolutional Neural Networks (CNNs) being the most used model for tasks such as dense prediction (or segmentation), detection and classification. In the same survey, which analyzed more than 300 contributions in the field, the authors found that computed tomography (CT) was the third most used imaging modality.
{"title":"Promises and limitations of deep learning for medical image segmentation","authors":"C. Perone, J. Cohen-Adad","doi":"10.21037/JMAI.2019.01.01","DOIUrl":"https://doi.org/10.21037/JMAI.2019.01.01","url":null,"abstract":"It is not a secret that recent advances in deep learning (1) methods have achieved a scientific and engineering milestone in many different fields such as natural language processing, computer vision, speech recognition, object detection, and segmentation, to name a few. Different applications of deep learning to medical imaging started to appear first in workshops, conferences and then in journals. According to a recent survey (2), the number of papers grew rapidly in 2015 and 2016. Nowadays, deep learning methods are pervasive throughout the entire medical imaging community, with Convolutional Neural Networks (CNNs) being the most used model for tasks such as dense prediction (or segmentation), detection and classification. In the same survey, which analyzed more than 300 contributions in the field, the authors found that computed tomography (CT) was the third most used imaging modality.","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.21037/JMAI.2019.01.01","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46836557","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}
Pub Date : 2018-12-01DOI: 10.21037/JMAI.2018.12.01
N. Boutros
As many as 33 per 100,000 people experience episodes of paroxysmal impairment associated with a range of manifestations that can be motor, sensory, and/or mental and closely mimic and frequently mistaken for epileptic seizures (1). These episodes are termed psychogenic non-epileptic seizures (PNES). The prevalence of PNES episodes is much higher in epilepsy practices, reaching as high as 30% (2). The diagnosis of PNES remains a process of excluding epilepsy and thus leads to an average time from onset of these paroxysms to diagnosis of close to seven years.
{"title":"Psychogenic non-epileptic seizures, recent advances and commentary on, Vasta et al., the application of artificial intelligence to understand the biological bases of the disorder","authors":"N. Boutros","doi":"10.21037/JMAI.2018.12.01","DOIUrl":"https://doi.org/10.21037/JMAI.2018.12.01","url":null,"abstract":"As many as 33 per 100,000 people experience episodes of paroxysmal impairment associated with a range of manifestations that can be motor, sensory, and/or mental and closely mimic and frequently mistaken for epileptic seizures (1). These episodes are termed psychogenic non-epileptic seizures (PNES). The prevalence of PNES episodes is much higher in epilepsy practices, reaching as high as 30% (2). The diagnosis of PNES remains a process of excluding epilepsy and thus leads to an average time from onset of these paroxysms to diagnosis of close to seven years.","PeriodicalId":73815,"journal":{"name":"Journal of medical artificial intelligence","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.21037/JMAI.2018.12.01","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43175427","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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