FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4387022045> ?p ?o ?g. }

• W4387022045 endingPage "469" @default.
• W4387022045 startingPage "468" @default.
• W4387022045 abstract "We thank Kapoor and Mahajan,[1] Fazal et al.,[2] and Gupta and Rangarajan[3] for their keen interest, valuable appreciation, and insightful comments on our article, “Artificial intelligence-based prediction of oral mucositis in patients with head-and-neck cancer: A prospective observational study utilizing a thermographic approach.”[4] We agree with Fazal et al.[1] that, when assessing oral mucositis, it is imperative to give due consideration to the patient’s clinical history and conduct a thorough physical examination. These aspects hold paramount importance in the evaluation process. At the Homi Bhabha Cancer Hospital, Tata Memorial Center, Sangrur, India, we maintain patient records both in the electronic medical record (EMR) system and physical files, while also conducting regular patient examinations. We completely acknowledge the validity of the comment by Fazal et al.[1] that doctors invest significant time in evaluating medical images, and the automation of thermal image processing with the help of artificial intelligence would reduce computational time.[5,6] In the future, more efforts should be made to improve the computational algorithms for larger datasets. We agree with the comments by Kapoor and Mahajan[2] that radiation-induced mucositis takes a minimum of 5–14 days to evolve, and thus, the data acquisition must be done within that specific time slot. Thermal imaging of patients with head-and-neck cancer was conducted over a four-week period as part of a preliminary (pilot) study. Our study[4] was cross-sectional, but thermal data were acquired every week; hence, in many cases, the thermal data were possibly from the same patient in consecutive weeks, but we did not document the data (details) on a weekly basis, which could have provided better clarity to the thermal data. Obtaining real-time data is an extremely time-consuming process, given the concurrent focus on uninterrupted treatment for patients with head-and-neck cancer during the data acquisition phase. The aim of our study[4] was to check the predictability of artificial intelligence-based thermal imaging for oral mucositis. We did not document the clinical aspects. To clarify, we included all cases that received a curative radical radiation dose of 70 Gy; we did not include any patients who received palliative radiotherapy. We agreed with the observation of Gupta and Rangarajan[3] that a larger sample size could have made the deep learning method more sensitive.[7] Real-time thermal data acquisition is a time-consuming process, and data acquisition is still ongoing. In the future, more efforts will be made to improve the computational algorithm on larger thermal datasets that contain images from patients with all grades of mucositis. We thank Gupta and Rangarajan[3] for their recommendations. We will go through the Medical Image Computing and Computer Assisted Intervention Society (MICCAI) and Checklist for Artificial Intelligence in Medical Imaging (CLAIM) checklists and ensure that we incorporate them in future studies.[8,9] We express our heartfelt gratitude for their valuable suggestions, which we hold in the highest regard. The inputs are immensely appreciated, and we are deeply thankful for the contributions. Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest." @default.
• W4387022045 created "2023-09-26" @default.
• W4387022045 creator A5043996386 @default.
• W4387022045 creator A5061310551 @default.
• W4387022045 creator A5073170842 @default.
• W4387022045 date "2023-01-01" @default.
• W4387022045 modified "2023-10-16" @default.
• W4387022045 title "Authors’ reply to Kapoor and Mahajan, Fazal et al., and Gupta and Rangarajan" @default.
• W4387022045 cites W1977931720 @default.
• W4387022045 cites W2561981131 @default.
• W4387022045 cites W2995118024 @default.
• W4387022045 cites W3013294478 @default.
• W4387022045 cites W4385494090 @default.
• W4387022045 cites W4387022043 @default.
• W4387022045 cites W4387022055 @default.
• W4387022045 doi "https://doi.org/10.4103/crst.crst_282_23" @default.
• W4387022045 hasPublicationYear "2023" @default.
• W4387022045 type Work @default.
• W4387022045 citedByCount "0" @default.
• W4387022045 crossrefType "journal-article" @default.
• W4387022045 hasAuthorship W4387022045A5043996386 @default.
• W4387022045 hasAuthorship W4387022045A5061310551 @default.
• W4387022045 hasAuthorship W4387022045A5073170842 @default.
• W4387022045 hasBestOaLocation W43870220451 @default.
• W4387022045 hasConcept C141071460 @default.
• W4387022045 hasConcept C142724271 @default.
• W4387022045 hasConcept C15744967 @default.
• W4387022045 hasConcept C19527891 @default.
• W4387022045 hasConcept C23131810 @default.
• W4387022045 hasConcept C2778496288 @default.
• W4387022045 hasConcept C41008148 @default.
• W4387022045 hasConcept C509550671 @default.
• W4387022045 hasConcept C509974204 @default.
• W4387022045 hasConcept C71924100 @default.
• W4387022045 hasConceptScore W4387022045C141071460 @default.
• W4387022045 hasConceptScore W4387022045C142724271 @default.
• W4387022045 hasConceptScore W4387022045C15744967 @default.
• W4387022045 hasConceptScore W4387022045C19527891 @default.
• W4387022045 hasConceptScore W4387022045C23131810 @default.
• W4387022045 hasConceptScore W4387022045C2778496288 @default.
• W4387022045 hasConceptScore W4387022045C41008148 @default.
• W4387022045 hasConceptScore W4387022045C509550671 @default.
• W4387022045 hasConceptScore W4387022045C509974204 @default.
• W4387022045 hasConceptScore W4387022045C71924100 @default.
• W4387022045 hasIssue "3" @default.
• W4387022045 hasLocation W43870220451 @default.
• W4387022045 hasOpenAccess W4387022045 @default.
• W4387022045 hasPrimaryLocation W43870220451 @default.
• W4387022045 hasRelatedWork W139966500 @default.
• W4387022045 hasRelatedWork W2092270052 @default.
• W4387022045 hasRelatedWork W2160416386 @default.
• W4387022045 hasRelatedWork W2274661527 @default.
• W4387022045 hasRelatedWork W2316222817 @default.
• W4387022045 hasRelatedWork W2748952813 @default.
• W4387022045 hasRelatedWork W2899084033 @default.
• W4387022045 hasRelatedWork W2936650130 @default.
• W4387022045 hasRelatedWork W3040604607 @default.
• W4387022045 hasRelatedWork W4285536504 @default.
• W4387022045 hasVolume "6" @default.
• W4387022045 isParatext "false" @default.
• W4387022045 isRetracted "false" @default.
• W4387022045 workType "article" @default.