Cost-effectiveness of artificial intelligence for screening colonoscopy: a modelling study

Areia, Miguel; Mori, Yuichi; Correale, Loredana; Repici, Alessandro; Bretthauer, Michael; Sharma, Prateek; Taveira, F; Spadaccini, Marco; Antonelli, Giulio; Ebigbo, Alanna; Kudo, Shin‐ei; Arribas, Julia; Barua, Ishita; Kamiński, Michał; Messmann, Helmut; Rex, Douglas K.; Dinis‐Ribeiro, Mário; Hassan, Cesare

doi:10.1016/s2589-7500(22)00042-5

Cited by 116 publications

(72 citation statements)

References 45 publications

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“…The study showed that using AI in breast screening for low-risk women is the most cost-effective solution. Areia et al [ 279 ] conducted the Markov model for colorectal cancer screening with and without AI. The study showed prevention of 7194 colorectal cancers along with prevention of 2089 deaths, and finally a saving of 290 million USD.…”

Section: Critical Discussionmentioning

confidence: 99%

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID-19: A Narrative Review

Suri¹,

Maindarkar

Sauseng

et al. 2022

Diagnostics

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Background and Motivation: Parkinson’s disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID-19 causes the ML systems to become severely non-linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well-explained ML paradigms. Deep neural networks are powerful learning machines that generalize non-linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID-19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID-19 framework. We study the hypothesis that PD in the presence of COVID-19 can cause more harm to the heart and brain than in non-COVID-19 conditions. COVID-19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID-19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID-19 lesions, office and laboratory arterial atherosclerotic image-based biomarkers, and medicine usage for the PD patients for the design of DL point-based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID-19 environment and this was also verified. DL architectures like long short-term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID-19.Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID-19.

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Section: Critical Discussionmentioning

confidence: 99%

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID-19: A Narrative Review

Suri¹,

Maindarkar

Sauseng

et al. 2022

Diagnostics

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“…The reimbursement and cost-effectiveness of AI are important concerns. In the field of colonoscopy, there are a few reports that suggest that the use of the CADe system may be cost-effective in reducing the incidence of colorectal cancer through improvement in adenoma detection and by reducing unnecessary polypectomy through accurate polyp differentiation [ 57 , 58 ]. Regarding the use of CAD systems for EGDS, its cost-effectiveness has not been fully verified so far; therefore, further research is expected in the near future.…”

Section: Discussionmentioning

confidence: 99%

Current Status of Artificial Intelligence-Based Computer-Assisted Diagnosis Systems for Gastric Cancer in Endoscopy

Ochiai

Ozawa

Shibata³

et al. 2022

Diagnostics

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Artificial intelligence (AI) is gradually being utilized in various fields as its performance has been improving with the development of deep learning methods, availability of big data, and the progression of computer processing units. In the field of medicine, AI is mainly implemented in image recognition, such as in radiographic and pathologic diagnoses. In the realm of gastrointestinal endoscopy, although AI-based computer-assisted detection/diagnosis (CAD) systems have been applied in some areas, such as colorectal polyp detection and diagnosis, so far, their implementation in real-world clinical settings is limited. The accurate detection or diagnosis of gastric cancer (GC) is one of the challenges in which performance varies greatly depending on the endoscopist’s skill. The diagnosis of early GC is especially challenging, partly because early GC mimics atrophic gastritis in the background mucosa. Therefore, several CAD systems for GC are being actively developed. The development of a CAD system for GC is considered challenging because it requires a large number of GC images. In particular, early stage GC images are rarely available, partly because it is difficult to diagnose gastric cancer during the early stages. Additionally, the training image data should be of a sufficiently high quality to conduct proper CAD training. Recently, several AI systems for GC that exhibit a robust performance, owing to being trained on a large number of high-quality images, have been reported. This review outlines the current status and prospects of AI use in esophagogastroduodenoscopy (EGDS), focusing on the diagnosis of GC.

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“…Prioritize reducing barriers to financial assistance and digitize traditionally paper-based applications to minimize processing and review times Widespread treatment-related financial harm [29] Financial toxicity Build evidence for payment innovation with the development of novel incentive structures to implement digital health technologies favoring value-based care Inadequate systemwide payment structures [34][35][36]…”

Section: Payer Policy and Reimbursementmentioning

confidence: 99%

Advancing Digital Health Innovation in Oncology: Priorities for High-Value Digital Transformation in Cancer Care

Patel¹,

Goldsack²,

Cordovano³

et al. 2023

J Med Internet Res

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Although health care delivery is becoming increasingly digitized, driven by the pursuit of improved access, equity, efficiency, and effectiveness, progress does not appear to be equally distributed across therapeutic areas. Oncology is renowned for leading innovation in research and in care; digital pathology, digital radiology, real-world data, next-generation sequencing, patient-reported outcomes, and precision approaches driven by complex data and biomarkers are hallmarks of the field. However, remote patient monitoring, decentralized approaches to care and research, “hospital at home,” and machine learning techniques have yet to be broadly deployed to improve cancer care. In response, the Digital Medicine Society and Moffitt Cancer Center convened a multistakeholder roundtable discussion to bring together leading experts in cancer care and digital innovation. This viewpoint highlights the findings from these discussions, in which experts agreed that digital innovation is lagging in oncology relative to other therapeutic areas. It reports that this lag is most likely attributed to poor articulation of the challenges in cancer care and research best suited to digital solutions, lack of incentives and support, and missing standardized infrastructure to implement digital innovations. It concludes with suggestions for actions needed to bring the promise of digitization to cancer care to improve lives.

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Cost-effectiveness of artificial intelligence for screening colonoscopy: a modelling study

Cited by 116 publications

References 45 publications

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID-19: A Narrative Review

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID-19: A Narrative Review

Current Status of Artificial Intelligence-Based Computer-Assisted Diagnosis Systems for Gastric Cancer in Endoscopy

Advancing Digital Health Innovation in Oncology: Priorities for High-Value Digital Transformation in Cancer Care

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