This paper presents our study of regularity for p-harmonic map heat flows. We devise a monotonicity-type formula of scaled energy and establish a criterion for a uniform regularity estimate for ...regular p-harmonic map heat flows. As application we show the small data global in the time existence of regular p-harmonic map heat flow.
In this paper, assuming the initial-boundary datum belonging to suitable Sobolev and Lebesgue spaces, we prove the global existence result for a (possibly sign changing) weak solution to the ...Cauchy–Dirichlet problem for doubly nonlinear parabolic equations of the form
∂
t
|
u
|
q
-
1
u
-
Δ
p
u
=
0
in
Ω
∞
,
where
p
>
1
and
q
>
0
. This is a fair improvement of the preceding result by authors (Nonlinear Anal 175C :157–172, 2018). The key tools we employ are energy estimates for approximate equations of Rothe type and the integral strong convergence of gradients of approximate solutions.
We show a global existence for the Cauchy problem with large initial data for the
p
-harmonic flow between two smooth, compact Riemannian manifolds. We devise new monotonicity type formulas of a ...local scaled energy and establish a partial regularity for the solution. The partial regularity obtained is almost optimal, comparing with that of the corresponding stationary case. The
p
-harmonic flow obtained also converges to a
p
-harmonic map along a certain time sequence tending to infinity.
Artificial intelligence (AI)–assisted polyp detection systems for colonoscopic use are currently attracting attention because they may reduce the possibility of missed adenomas. However, few systems ...have the necessary regulatory approval for use in clinical practice. We aimed to develop an AI-assisted polyp detection system and to validate its performance using a large colonoscopy video database designed to be publicly accessible.
To develop the deep learning–based AI system, 56,668 independent colonoscopy images were obtained from 5 centers for use as training images. To validate the trained AI system, consecutive colonoscopy videos taken at a university hospital between October 2018 and January 2019 were searched to construct a database containing polyps with unbiased variance. All images were annotated by endoscopists according to the presence or absence of polyps and the polyps’ locations with bounding boxes.
A total of 1405 videos acquired during the study period were identified for the validation database, 797 of which contained at least 1 polyp. Of these, 100 videos containing 100 independent polyps and 13 videos negative for polyps were randomly extracted, resulting in 152,560 frames (49,799 positive frames and 102,761 negative frames) for the database. The AI showed 90.5% sensitivity and 93.7% specificity for frame-based analysis. The per-polyp sensitivities for all, diminutive, protruded, and flat polyps were 98.0%, 98.3%, 98.5%, and 97.0%, respectively.
Our trained AI system was validated with a new large publicly accessible colonoscopy database and could identify colorectal lesions with high sensitivity and specificity. (Clinical trial registration number: UMIN 000037064.)
Artificial intelligence (AI)-based polyp detection systems are used during colonoscopy with the aim of increasing lesion detection and improving colonoscopy quality.
We performed a systematic review ...and meta-analysis of prospective trials to determine the value of AI-based polyp detection systems for detection of polyps and colorectal cancer. We performed systematic searches in MEDLINE, EMBASE, and Cochrane CENTRAL. Independent reviewers screened studies and assessed eligibility, certainty of evidence, and risk of bias. We compared colonoscopy with and without AI by calculating relative and absolute risks and mean differences for detection of polyps, adenomas, and colorectal cancer.
Five randomized trials were eligible for analysis. Colonoscopy with AI increased adenoma detection rates (ADRs) and polyp detection rates (PDRs) compared to colonoscopy without AI (values given with 95 %CI). ADR with AI was 29.6 % (22.2 % - 37.0 %) versus 19.3 % (12.7 % - 25.9 %) without AI; relative risk (RR 1.52 (1.31 - 1.77), with high certainty. PDR was 45.4 % (41.1 % - 49.8 %) with AI versus 30.6 % (26.5 % - 34.6 %) without AI; RR 1.48 (1.37 - 1.60), with high certainty. There was no difference in detection of advanced adenomas (mean advanced adenomas per colonoscopy 0.03 for each group, high certainty). Mean adenomas detected per colonoscopy was higher for small adenomas (≤ 5 mm) for AI versus non-AI (mean difference 0.15 0.12 - 0.18), but not for larger adenomas (> 5 - ≤ 10 mm, mean difference 0.03 0.01 - 0.05; > 10 mm, mean difference 0.01 0.00 - 0.02; high certainty). Data on cancer are unavailable.
AI-based polyp detection systems during colonoscopy increase detection of small nonadvanced adenomas and polyps, but not of advanced adenomas.
Background and Aim
Application of artificial intelligence in medicine is now attracting substantial attention. In the field of gastrointestinal endoscopy, computer‐aided diagnosis (CAD) for ...colonoscopy is the most investigated area, although it is still in the preclinical phase. Because colonoscopy is carried out by humans, it is inherently an imperfect procedure. CAD assistance is expected to improve its quality regarding automated polyp detection and characterization (i.e. predicting the polyp's pathology). It could help prevent endoscopists from missing polyps as well as provide a precise optical diagnosis for those detected. Ultimately, these functions that CAD provides could produce a higher adenoma detection rate and reduce the cost of polypectomy for hyperplastic polyps.
Methods and Results
Currently, research on automated polyp detection has been limited to experimental assessments using an algorithm based on ex vivo videos or static images. Performance for clinical use was reported to have >90% sensitivity with acceptable specificity. In contrast, research on automated polyp characterization seems to surpass that for polyp detection. Prospective studies of in vivo use of artificial intelligence technologies have been reported by several groups, some of which showed a >90% negative predictive value for differentiating diminutive (≤5 mm) rectosigmoid adenomas, which exceeded the threshold for optical biopsy.
Conclusion
We introduce the potential of using CAD for colonoscopy and describe the most recent conditions for regulatory approval for artificial intelligence‐assisted medical devices.
Adoption of artificial intelligence (AI) in clinical medicine is revolutionizing daily practice. In the field of colonoscopy, major endoscopy manufacturers have already launched their own AI products ...on the market with regulatory approval in Europe and Asia. This commercialization is strongly supported by positive evidence that has been recently established through rigorously designed prospective trials and randomized controlled trials. According to some of the trials, AI tools possibly increase the adenoma detection rate by roughly 50% and contribute to a 7–20% reduction of colonoscopy‐related costs. Given that reliable evidence is emerging, together with active commercialization, this seems to be a good time for us to review and discuss the current status of AI in colonoscopy from a clinical perspective. In this review, we introduce the advantages and possible drawbacks of AI tools and explore their future potential including the possibility of obtaining reimbursement.
With recent breakthroughs in artificial intelligence, computer‐aided diagnosis (CAD) for upper gastrointestinal endoscopy is gaining increasing attention. Main research focuses in this field include ...automated identification of dysplasia in Barrett's esophagus and detection of early gastric cancers. By helping endoscopists avoid missing and mischaracterizing neoplastic change in both the esophagus and the stomach, these technologies potentially contribute to solving current limitations of gastroscopy. Currently, optical diagnosis of early‐stage dysplasia related to Barrett's esophagus can be precisely achieved only by endoscopists proficient in advanced endoscopic imaging, and the false‐negative rate for detecting gastric cancer is approximately 10%. Ideally, these novel technologies should work during real‐time gastroscopy to provide on‐site decision support for endoscopists regardless of their skill; however, previous studies of these topics remain ex vivo and experimental in design. Therefore, the feasibility, effectiveness, and safety of CAD for upper gastrointestinal endoscopy in clinical practice remain unknown, although a considerable number of pilot studies have been conducted by both engineers and medical doctors with excellent results. This review summarizes current publications relating to CAD for upper gastrointestinal endoscopy from the perspective of endoscopists and aims to indicate what is required for future research and implementation in clinical practice.
We study a geometric analysis and local regularity for
the evolution of
-harmonic maps, called
-harmonic map heat flows.
Our main result is
to establish a criterion for a uniform local regularity ...estimate
for regular
-harmonic map heat flows,
devising some new monotonicity-type formulas
of a local scaled energy.
The regularity criterion obtained is almost optimal, comparing with
that of the corresponding stationary case.
As application we show a compactness of
regular
-harmonic map heat flows with energy bound.
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