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The Lancet. Digital Health Jun 2022Skin cancers occur commonly worldwide. The prognosis and disease burden are highly dependent on the cancer type and disease stage at diagnosis. We systematically... (Review)
Review
Skin cancers occur commonly worldwide. The prognosis and disease burden are highly dependent on the cancer type and disease stage at diagnosis. We systematically reviewed studies on artificial intelligence and machine learning (AI/ML) algorithms that aim to facilitate the early diagnosis of skin cancers, focusing on their application in primary and community care settings. We searched MEDLINE, Embase, Scopus, and Web of Science (from Jan 1, 2000, to Aug 9, 2021) for all studies providing evidence on applying AI/ML algorithms to the early diagnosis of skin cancer, including all study designs and languages. The primary outcome was diagnostic accuracy of the algorithms for skin cancers. The secondary outcomes included an overview of AI/ML methods, evaluation approaches, cost-effectiveness, and acceptability to patients and clinicians. We identified 14 224 studies. Only two studies used data from clinical settings with a low prevalence of skin cancers. We reported data from all 272 studies that could be relevant in primary care. The primary outcomes showed reasonable mean diagnostic accuracy for melanoma (89·5% [range 59·7-100%]), squamous cell carcinoma (85·3% [71·0-97·8%]), and basal cell carcinoma (87·6% [70·0-99·7%]). The secondary outcomes showed a heterogeneity of AI/ML methods and study designs, with high amounts of incomplete reporting (eg, patient demographics and methods of data collection). Few studies used data on populations with a low prevalence of skin cancers to train and test their algorithms; therefore, the widespread adoption into community and primary care practice cannot currently be recommended until efficacy in these populations is shown. We did not identify any health economic, patient, or clinician acceptability data for any of the included studies. We propose a methodological checklist for use in the development of new AI/ML algorithms to detect skin cancer, to facilitate their design, evaluation, and implementation.
Topics: Algorithms; Artificial Intelligence; Early Detection of Cancer; Humans; Machine Learning; Primary Health Care; Skin Neoplasms
PubMed: 35623799
DOI: 10.1016/S2589-7500(22)00023-1 -
Journal of Ambient Intelligence and... 2023Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine...
Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score.
PubMed: 35039756
DOI: 10.1007/s12652-021-03612-z -
European Journal of Cancer (Oxford,... Sep 2021Gastrointestinal cancers account for approximately 20% of all cancer diagnoses and are responsible for 22.5% of cancer deaths worldwide. Artificial intelligence-based...
BACKGROUND
Gastrointestinal cancers account for approximately 20% of all cancer diagnoses and are responsible for 22.5% of cancer deaths worldwide. Artificial intelligence-based diagnostic support systems, in particular convolutional neural network (CNN)-based image analysis tools, have shown great potential in medical computer vision. In this systematic review, we summarise recent studies reporting CNN-based approaches for digital biomarkers for characterization and prognostication of gastrointestinal cancer pathology.
METHODS
Pubmed and Medline were screened for peer-reviewed papers dealing with CNN-based gastrointestinal cancer analyses from histological slides, published between 2015 and 2020.Seven hundred and ninety titles and abstracts were screened, and 58 full-text articles were assessed for eligibility.
RESULTS
Sixteen publications fulfilled our inclusion criteria dealing with tumor or precursor lesion characterization or prognostic and predictive biomarkers: 14 studies on colorectal or rectal cancer, three studies on gastric cancer and none on esophageal cancer. These studies were categorised according to their end-points: polyp characterization, tumor characterization and patient outcome. Regarding the translation into clinical practice, we identified several studies demonstrating generalization of the classifier with external tests and comparisons with pathologists, but none presenting clinical implementation.
CONCLUSIONS
Results of recent studies on CNN-based image analysis in gastrointestinal cancer pathology are promising, but studies were conducted in observational and retrospective settings. Large-scale trials are needed to assess performance and predict clinical usefulness. Furthermore, large-scale trials are required for approval of CNN-based prediction models as medical devices.
Topics: Deep Learning; Gastrointestinal Neoplasms; Humans; Treatment Outcome
PubMed: 34391053
DOI: 10.1016/j.ejca.2021.07.012 -
The Cochrane Database of Systematic... Mar 2021High-flow nasal cannulae (HFNC) deliver high flows of blended humidified air and oxygen via wide-bore nasal cannulae and may be useful in providing respiratory support... (Meta-Analysis)
Meta-Analysis
BACKGROUND
High-flow nasal cannulae (HFNC) deliver high flows of blended humidified air and oxygen via wide-bore nasal cannulae and may be useful in providing respiratory support for adults experiencing acute respiratory failure, or at risk of acute respiratory failure, in the intensive care unit (ICU). This is an update of an earlier version of the review.
OBJECTIVES
To assess the effectiveness of HFNC compared to standard oxygen therapy, or non-invasive ventilation (NIV) or non-invasive positive pressure ventilation (NIPPV), for respiratory support in adults in the ICU.
SEARCH METHODS
We searched CENTRAL, MEDLINE, Embase, CINAHL, Web of Science, and the Cochrane COVID-19 Register (17 April 2020), clinical trial registers (6 April 2020) and conducted forward and backward citation searches.
SELECTION CRITERIA
We included randomized controlled studies (RCTs) with a parallel-group or cross-over design comparing HFNC use versus other types of non-invasive respiratory support (standard oxygen therapy via nasal cannulae or mask; or NIV or NIPPV which included continuous positive airway pressure and bilevel positive airway pressure) in adults admitted to the ICU.
DATA COLLECTION AND ANALYSIS
We used standard methodological procedures as expected by Cochrane.
MAIN RESULTS
We included 31 studies (22 parallel-group and nine cross-over designs) with 5136 participants; this update included 20 new studies. Twenty-one studies compared HFNC with standard oxygen therapy, and 13 compared HFNC with NIV or NIPPV; three studies included both comparisons. We found 51 ongoing studies (estimated 12,807 participants), and 19 studies awaiting classification for which we could not ascertain study eligibility information. In 18 studies, treatment was initiated after extubation. In the remaining studies, participants were not previously mechanically ventilated. HFNC versus standard oxygen therapy HFNC may lead to less treatment failure as indicated by escalation to alternative types of oxygen therapy (risk ratio (RR) 0.62, 95% confidence interval (CI) 0.45 to 0.86; 15 studies, 3044 participants; low-certainty evidence). HFNC probably makes little or no difference in mortality when compared with standard oxygen therapy (RR 0.96, 95% CI 0.82 to 1.11; 11 studies, 2673 participants; moderate-certainty evidence). HFNC probably results in little or no difference to cases of pneumonia (RR 0.72, 95% CI 0.48 to 1.09; 4 studies, 1057 participants; moderate-certainty evidence), and we were uncertain of its effect on nasal mucosa or skin trauma (RR 3.66, 95% CI 0.43 to 31.48; 2 studies, 617 participants; very low-certainty evidence). We found low-certainty evidence that HFNC may make little or no difference to the length of ICU stay according to the type of respiratory support used (MD 0.12 days, 95% CI -0.03 to 0.27; 7 studies, 1014 participants). We are uncertain whether HFNC made any difference to the ratio of partial pressure of arterial oxygen to the fraction of inspired oxygen (PaO/FiO) within 24 hours of treatment (MD 10.34 mmHg, 95% CI -17.31 to 38; 5 studies, 600 participants; very low-certainty evidence). We are uncertain whether HFNC made any difference to short-term comfort (MD 0.31, 95% CI -0.60 to 1.22; 4 studies, 662 participants, very low-certainty evidence), or to long-term comfort (MD 0.59, 95% CI -2.29 to 3.47; 2 studies, 445 participants, very low-certainty evidence). HFNC versus NIV or NIPPV We found no evidence of a difference between groups in treatment failure when HFNC were used post-extubation or without prior use of mechanical ventilation (RR 0.98, 95% CI 0.78 to 1.22; 5 studies, 1758 participants; low-certainty evidence), or in-hospital mortality (RR 0.92, 95% CI 0.64 to 1.31; 5 studies, 1758 participants; low-certainty evidence). We are very uncertain about the effect of using HFNC on incidence of pneumonia (RR 0.51, 95% CI 0.17 to 1.52; 3 studies, 1750 participants; very low-certainty evidence), and HFNC may result in little or no difference to barotrauma (RR 1.15, 95% CI 0.42 to 3.14; 1 study, 830 participants; low-certainty evidence). HFNC may make little or no difference to the length of ICU stay (MD -0.72 days, 95% CI -2.85 to 1.42; 2 studies, 246 participants; low-certainty evidence). The ratio of PaO/FiO may be lower up to 24 hours with HFNC use (MD -58.10 mmHg, 95% CI -71.68 to -44.51; 3 studies, 1086 participants; low-certainty evidence). We are uncertain whether HFNC improved short-term comfort when measured using comfort scores (MD 1.33, 95% CI 0.74 to 1.92; 2 studies, 258 participants) and responses to questionnaires (RR 1.30, 95% CI 1.10 to 1.53; 1 study, 168 participants); evidence for short-term comfort was very low certainty. No studies reported on nasal mucosa or skin trauma.
AUTHORS' CONCLUSIONS
HFNC may lead to less treatment failure when compared to standard oxygen therapy, but probably makes little or no difference to treatment failure when compared to NIV or NIPPV. For most other review outcomes, we found no evidence of a difference in effect. However, the evidence was often of low or very low certainty. We found a large number of ongoing studies; including these in future updates could increase the certainty or may alter the direction of these effects.
Topics: Acute Disease; Adult; Barotrauma; Bias; Critical Care; Hospital Mortality; Humans; Intubation; Length of Stay; Masks; Nasal Mucosa; Noninvasive Ventilation; Oxygen Inhalation Therapy; Patient Reported Outcome Measures; Pneumonia; Randomized Controlled Trials as Topic; Respiration, Artificial; Respiratory Insufficiency; Treatment Failure
PubMed: 33661521
DOI: 10.1002/14651858.CD010172.pub3 -
Diagnostics (Basel, Switzerland) Oct 2023Skin lesions are essential for the early detection and management of a number of dermatological disorders. Learning-based methods for skin lesion analysis have drawn... (Review)
Review
Skin lesions are essential for the early detection and management of a number of dermatological disorders. Learning-based methods for skin lesion analysis have drawn much attention lately because of improvements in computer vision and machine learning techniques. A review of the most-recent methods for skin lesion classification, segmentation, and detection is presented in this survey paper. The significance of skin lesion analysis in healthcare and the difficulties of physical inspection are discussed in this survey paper. The review of state-of-the-art papers targeting skin lesion classification is then covered in depth with the goal of correctly identifying the type of skin lesion from dermoscopic, macroscopic, and other lesion image formats. The contribution and limitations of various techniques used in the selected study papers, including deep learning architectures and conventional machine learning methods, are examined. The survey then looks into study papers focused on skin lesion segmentation and detection techniques that aimed to identify the precise borders of skin lesions and classify them accordingly. These techniques make it easier to conduct subsequent analyses and allow for precise measurements and quantitative evaluations. The survey paper discusses well-known segmentation algorithms, including deep-learning-based, graph-based, and region-based ones. The difficulties, datasets, and evaluation metrics particular to skin lesion segmentation are also discussed. Throughout the survey, notable datasets, benchmark challenges, and evaluation metrics relevant to skin lesion analysis are highlighted, providing a comprehensive overview of the field. The paper concludes with a summary of the major trends, challenges, and potential future directions in skin lesion classification, segmentation, and detection, aiming to inspire further advancements in this critical domain of dermatological research.
PubMed: 37835889
DOI: 10.3390/diagnostics13193147 -
European Journal of Cancer (Oxford,... May 2022Due to their ability to solve complex problems, deep neural networks (DNNs) are becoming increasingly popular in medical applications. However, decision-making by such... (Review)
Review
BACKGROUND
Due to their ability to solve complex problems, deep neural networks (DNNs) are becoming increasingly popular in medical applications. However, decision-making by such algorithms is essentially a black-box process that renders it difficult for physicians to judge whether the decisions are reliable. The use of explainable artificial intelligence (XAI) is often suggested as a solution to this problem. We investigate how XAI is used for skin cancer detection: how is it used during the development of new DNNs? What kinds of visualisations are commonly used? Are there systematic evaluations of XAI with dermatologists or dermatopathologists?
METHODS
Google Scholar, PubMed, IEEE Explore, Science Direct and Scopus were searched for peer-reviewed studies published between January 2017 and October 2021 applying XAI to dermatological images: the search terms histopathological image, whole-slide image, clinical image, dermoscopic image, skin, dermatology, explainable, interpretable and XAI were used in various combinations. Only studies concerned with skin cancer were included.
RESULTS
37 publications fulfilled our inclusion criteria. Most studies (19/37) simply applied existing XAI methods to their classifier to interpret its decision-making. Some studies (4/37) proposed new XAI methods or improved upon existing techniques. 14/37 studies addressed specific questions such as bias detection and impact of XAI on man-machine-interactions. However, only three of them evaluated the performance and confidence of humans using CAD systems with XAI.
CONCLUSION
XAI is commonly applied during the development of DNNs for skin cancer detection. However, a systematic and rigorous evaluation of its usefulness in this scenario is lacking.
Topics: Algorithms; Artificial Intelligence; Humans; Neural Networks, Computer; Skin Neoplasms
PubMed: 35390650
DOI: 10.1016/j.ejca.2022.02.025 -
Journal of Medical Internet Research Nov 2021Skin cancer is the most common cancer type affecting humans. Traditional skin cancer diagnosis methods are costly, require a professional physician, and take time.... (Review)
Review
BACKGROUND
Skin cancer is the most common cancer type affecting humans. Traditional skin cancer diagnosis methods are costly, require a professional physician, and take time. Hence, to aid in diagnosing skin cancer, artificial intelligence (AI) tools are being used, including shallow and deep machine learning-based methodologies that are trained to detect and classify skin cancer using computer algorithms and deep neural networks.
OBJECTIVE
The aim of this study was to identify and group the different types of AI-based technologies used to detect and classify skin cancer. The study also examined the reliability of the selected papers by studying the correlation between the data set size and the number of diagnostic classes with the performance metrics used to evaluate the models.
METHODS
We conducted a systematic search for papers using Institute of Electrical and Electronics Engineers (IEEE) Xplore, Association for Computing Machinery Digital Library (ACM DL), and Ovid MEDLINE databases following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. The studies included in this scoping review had to fulfill several selection criteria: being specifically about skin cancer, detecting or classifying skin cancer, and using AI technologies. Study selection and data extraction were independently conducted by two reviewers. Extracted data were narratively synthesized, where studies were grouped based on the diagnostic AI techniques and their evaluation metrics.
RESULTS
We retrieved 906 papers from the 3 databases, of which 53 were eligible for this review. Shallow AI-based techniques were used in 14 studies, and deep AI-based techniques were used in 39 studies. The studies used up to 11 evaluation metrics to assess the proposed models, where 39 studies used accuracy as the primary evaluation metric. Overall, studies that used smaller data sets reported higher accuracy.
CONCLUSIONS
This paper examined multiple AI-based skin cancer detection models. However, a direct comparison between methods was hindered by the varied use of different evaluation metrics and image types. Performance scores were affected by factors such as data set size, number of diagnostic classes, and techniques. Hence, the reliability of shallow and deep models with higher accuracy scores was questionable since they were trained and tested on relatively small data sets of a few diagnostic classes.
Topics: Algorithms; Artificial Intelligence; Data Management; Humans; Reproducibility of Results; Skin Neoplasms
PubMed: 34821566
DOI: 10.2196/22934 -
Journal of Clinical Medicine Dec 2023Leprosy is a neglected tropical disease that can cause physical injury and mental disability. Diagnosis is primarily clinical, but can be inconclusive due to the absence... (Review)
Review
Leprosy is a neglected tropical disease that can cause physical injury and mental disability. Diagnosis is primarily clinical, but can be inconclusive due to the absence of initial symptoms and similarity to other dermatological diseases. Artificial intelligence (AI) techniques have been used in dermatology, assisting clinical procedures and diagnostics. In particular, AI-supported solutions have been proposed in the literature to aid in the diagnosis of leprosy, and this Systematic Literature Review (SLR) aims to characterize the state of the art. This SLR followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework and was conducted in the following databases: ACM Digital Library, IEEE Digital Library, ISI Web of Science, Scopus, and PubMed. Potentially relevant research articles were retrieved. The researchers applied criteria to select the studies, assess their quality, and perform the data extraction process. Moreover, 1659 studies were retrieved, of which 21 were included in the review after selection. Most of the studies used images of skin lesions, classical machine learning algorithms, and multi-class classification tasks to develop models to diagnose dermatological diseases. Most of the reviewed articles did not target leprosy as the study's primary objective but rather the classification of different skin diseases (among them, leprosy). Although AI-supported leprosy diagnosis is constantly evolving, research in this area is still in its early stage, then studies are required to make AI solutions mature enough to be transformed into clinical practice. Expanding research efforts on leprosy diagnosis, coupled with the advocacy of open science in leveraging AI for diagnostic support, can yield robust and influential outcomes.
PubMed: 38202187
DOI: 10.3390/jcm13010180 -
NPJ Digital Medicine Apr 2024The development of diagnostic tools for skin cancer based on artificial intelligence (AI) is increasing rapidly and will likely soon be widely implemented in clinical... (Review)
Review
The development of diagnostic tools for skin cancer based on artificial intelligence (AI) is increasing rapidly and will likely soon be widely implemented in clinical use. Even though the performance of these algorithms is promising in theory, there is limited evidence on the impact of AI assistance on human diagnostic decisions. Therefore, the aim of this systematic review and meta-analysis was to study the effect of AI assistance on the accuracy of skin cancer diagnosis. We searched PubMed, Embase, IEE Xplore, Scopus and conference proceedings for articles from 1/1/2017 to 11/8/2022. We included studies comparing the performance of clinicians diagnosing at least one skin cancer with and without deep learning-based AI assistance. Summary estimates of sensitivity and specificity of diagnostic accuracy with versus without AI assistance were computed using a bivariate random effects model. We identified 2983 studies, of which ten were eligible for meta-analysis. For clinicians without AI assistance, pooled sensitivity was 74.8% (95% CI 68.6-80.1) and specificity was 81.5% (95% CI 73.9-87.3). For AI-assisted clinicians, the overall sensitivity was 81.1% (95% CI 74.4-86.5) and specificity was 86.1% (95% CI 79.2-90.9). AI benefitted medical professionals of all experience levels in subgroup analyses, with the largest improvement among non-dermatologists. No publication bias was detected, and sensitivity analysis revealed that the findings were robust. AI in the hands of clinicians has the potential to improve diagnostic accuracy in skin cancer diagnosis. Given that most studies were conducted in experimental settings, we encourage future studies to further investigate these potential benefits in real-life settings.
PubMed: 38594408
DOI: 10.1038/s41746-024-01031-w -
Brain Sciences Apr 2023Neuro-tourism is the application of neuroscience in tourism to improve marketing methods of the tourism industry by analyzing the brain activities of tourists.... (Review)
Review
Neuro-tourism is the application of neuroscience in tourism to improve marketing methods of the tourism industry by analyzing the brain activities of tourists. Neuro-tourism provides accurate real-time data on tourists' conscious and unconscious emotions. Neuro-tourism uses the methods of neuromarketing such as brain-computer interface (BCI), eye-tracking, galvanic skin response, etc., to create tourism goods and services to improve tourist experience and satisfaction. Due to the novelty of neuro-tourism and the dearth of studies on this subject, this study offered a comprehensive analysis of the peer-reviewed journal publications in neuro-tourism research for the previous 12 years to detect trends in this field and provide insights for academics. We reviewed 52 articles indexed in the Web of Science (WoS) core collection database and examined them using our suggested classification schema. The results reveal a large growth in the number of published articles on neuro-tourism, demonstrating a rise in the relevance of this field. Additionally, the findings indicated a lack of integrating artificial intelligence techniques in neuro-tourism studies. We believe that the advancements in technology and research collaboration will facilitate exponential growth in this field.
PubMed: 37190647
DOI: 10.3390/brainsci13040682