Decoding Deep Learning applications for diagnosis and treatment planning

Retrouvey, Jean Marc; Conley, R. Scott

doi:10.1590/2177-6709.27.5.e22spe5

Cited by 4 publications

(1 citation statement)

References 62 publications

(47 reference statements)

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“…Currently, AI technologies are used in diagnosis of disease, surgical assistance, data sorting in an intensive care unit, prediction of prognosis, and drug development 5) . However, many of these technologies are seem to be still investigative 6) .…”

Section: Artificial Intelligence (Ai) For Neurosurgery : Current Stat...mentioning

confidence: 99%

Editors’ Pick in March 2023

Chung

2023

J Korean Neurosurg Soc

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Section: Artificial Intelligence (Ai) For Neurosurgery : Current Stat...mentioning

confidence: 99%

Editors’ Pick in March 2023

Chung

2023

J Korean Neurosurg Soc

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AI and Face-Driven Orthodontics: A Scoping Review of Digital Advances in Diagnosis and Treatment Planning

Tomášik,

Zsoldos,

Oravcová

et al. 2024

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In the age of artificial intelligence (AI), technological progress is changing established workflows and enabling some basic routines to be updated. In dentistry, the patient’s face is a crucial part of treatment planning, although it has always been difficult to grasp in an analytical way. This review highlights the current digital advances that, thanks to AI tools, allow us to implement facial features beyond symmetry and proportionality and incorporate facial analysis into diagnosis and treatment planning in orthodontics. A Scopus literature search was conducted to identify the topics with the greatest research potential within digital orthodontics over the last five years. The most researched and cited topic was artificial intelligence and its applications in orthodontics. Apart from automated 2D or 3D cephalometric analysis, AI finds its application in facial analysis, decision-making algorithms as well as in the evaluation of treatment progress and retention. Together with AI, other digital advances are shaping the face of today’s orthodontics. Without any doubts, the era of “old” orthodontics is at its end, and modern, face-driven orthodontics is on the way to becoming a reality in modern orthodontic practices.

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Deep Learning for Medical Image Cryptography: A Comprehensive Review

Lata

Cenkeramaddi

2023

Applied Sciences

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Electronic health records (EHRs) security is a critical challenge in the implementation and administration of Internet of Medical Things (IoMT) systems within the healthcare sector’s heterogeneous environment. As digital transformation continues to advance, ensuring privacy, integrity, and availability of EHRs become increasingly complex. Various imaging modalities, including PET, MRI, ultrasonography, CT, and X-ray imaging, play vital roles in medical diagnosis, allowing healthcare professionals to visualize and assess the internal structures, functions, and abnormalities within the human body. These diagnostic images are typically stored, shared, and processed for various purposes, including segmentation, feature selection, and image denoising. Cryptography techniques offer a promising solution for protecting sensitive medical image data during storage and transmission. Deep learning has the potential to revolutionize cryptography techniques for securing medical images. This paper explores the application of deep learning techniques in medical image cryptography, aiming to enhance the privacy and security of healthcare data. It investigates the use of deep learning models for image encryption, image resolution enhancement, detection and classification, encrypted compression, key generation, and end-to-end encryption. Finally, we provide insights into the current research challenges and promising directions for future research in the field of deep learning applications in medical image cryptography.

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Decoding Deep Learning applications for diagnosis and treatment planning

Cited by 4 publications

References 62 publications

Editors’ Pick in March 2023

Editors’ Pick in March 2023

AI and Face-Driven Orthodontics: A Scoping Review of Digital Advances in Diagnosis and Treatment Planning

Deep Learning for Medical Image Cryptography: A Comprehensive Review

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