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Peer Review of “Dental Tissue Density in Healthy Children Based on Radiological Data: Retrospective Analysis”

Peer Review of “Dental Tissue Density in Healthy Children Based on Radiological Data: Retrospective Analysis”

This is the peer-review report for “Dental Tissue Density in Healthy Children Based on Radiological Data: Retrospective Analysis.” This paper [1] has a very good topic selected by the authors. Based on the study of Hounsfield units in cone-beam computed tomography (CBCT), we can evaluate conditions that are abnormal in patients. There is not much to comment on, but try to include good pictures of the CBCT. 1. The authors did not mention the ideal values of the Hounsfield units for enamel and dentin. 2.

Shanmukha Gorthy

JMIRx Med 2024;5:e60329

Peer Review of “Dental Tissue Density in Healthy Children Based on Radiological Data: Retrospective Analysis”

Peer Review of “Dental Tissue Density in Healthy Children Based on Radiological Data: Retrospective Analysis”

This is the peer-review report for ”Dental Tissue Density in Healthy Children Based on Radiological Data: Retrospective Analysis.” The subject is interesting. The densities of dental hard tissues were determined by cone-beam computed tomography (CBCT), a technique that has been recently used for this purpose. 1. The article [1] specifies the aim and is structured according to the journal’s recommendations. 2.

Anonymous

JMIRx Med 2024;5:e62676

Evaluation of GPT-4’s Chest X-Ray Impression Generation: A Reader Study on Performance and Perception

Evaluation of GPT-4’s Chest X-Ray Impression Generation: A Reader Study on Performance and Perception

Therefore, our study investigated the ability of GPT-4 to generate radiological impressions based on different inputs, focusing on the correlation between radiological assessment of impression quality and common automated evaluation metrics, as well as radiological perception of AI-generated text.

Sebastian Ziegelmayer, Alexander W Marka, Nicolas Lenhart, Nadja Nehls, Stefan Reischl, Felix Harder, Andreas Sauter, Marcus Makowski, Markus Graf, Joshua Gawlitza

J Med Internet Res 2023;25:e50865

Open-Source Intelligence for Detection of Radiological Events and Syndromes Following the Invasion of Ukraine in 2022: Observational Study

Open-Source Intelligence for Detection of Radiological Events and Syndromes Following the Invasion of Ukraine in 2022: Observational Study

This highlighted the possibility of contamination of previously uncontaminated areas and the potential for subsequent radiological impacts on human and environmental health. Epidemic open-source intelligence (OSINT) systems provide new approaches to public health surveillance and are increasingly used for epidemic early warning [6].

Haley Stone, David Heslop, Samsung Lim, Ines Sarmiento, Mohana Kunasekaran, C Raina MacIntyre

JMIR Infodemiology 2023;3:e39895

Developing a Risk Governance Framework on Radiological Emergency, Preparedness, and Response for Emergency Responders: Protocol for a Mixed Methods Study

Developing a Risk Governance Framework on Radiological Emergency, Preparedness, and Response for Emergency Responders: Protocol for a Mixed Methods Study

To establish a system in radiological emergency preparedness and response (EPR), the International Atomic Energy Agency (IAEA) has developed a few documents that recommend what forms the basis of and the requirements for an adequate level of preparedness and response for a nuclear or radiological emergency.

Anita Abd Rahman, Rosliza Abdul Manaf, Poh Ying Lim, Subapriya Suppiah, Muhammad Hanafiah Juni

JMIR Res Protoc 2021;10(8):e25877