Fracture Forecasting through Deep Learning's Role in Bone Injury Detection and Classification DOI

Mohit Beri,

Kanwarpartap Singh Gill, Deepak Upadhyay

и другие.

Опубликована: Июль 27, 2024

Язык: Английский

Dermatology and artificial intelligence DOI
W Clark Lambert, Andrzej Grzybowski

Clinics in Dermatology, Год журнала: 2024, Номер 42(3), С. 207 - 209

Опубликована: Янв. 4, 2024

Язык: Английский

Процитировано

5
Healthcare Systems and Artificial Intelligence: Focus on Challenges and the International Regulatory Framework DOI
Alessia Romagnoli, Francesco Ferrara,

Roberto Langella

и другие.

Pharmaceutical Research, Год журнала: 2024, Номер 41(4), С. 721 - 730

Опубликована: Март 5, 2024

Язык: Английский

Процитировано

5
Automatic Measurement and Comparison of Normal Eyelid Contour by Age and Gender Using Image-Based Deep Learning DOI Creative Commons

Ji Shao,

Jing Cao, Changjun Wang

и другие.

Ophthalmology Science, Год журнала: 2024, Номер 4(5), С. 100518 - 100518

Опубликована: Март 22, 2024

This study aimed to propose a fully automatic eyelid measurement system and compare the contours of both upper lower eyelids normal individuals according age gender.

Язык: Английский

Процитировано

4
Artificial Intelligence in Oculoplastics: A Survey-Based Study on Provider Perspective DOI Open Access

Balázs Fazekas,

Malik Moledina,

Nehal Singhania

и другие.

Cureus, Год журнала: 2025, Номер unknown

Опубликована: Март 27, 2025

Язык: Английский

Процитировано

0
Reliability and Agreement of Free Web-Based 3D Software for Computing Facial Area and Volume Measurements DOI Creative Commons
Oğuzhan Topsakal,

Philip N. Sawyer,

Tahir Çetin Akıncı

и другие.

BioMedInformatics, Год журнала: 2024, Номер 4(1), С. 690 - 708

Опубликована: Март 1, 2024

Background: Facial surgeries require meticulous planning and outcome assessments, where facial analysis plays a critical role. This study introduces new approach by utilizing three-dimensional (3D) imaging techniques, which are known for their ability to measure areas volumes accurately. The purpose of this is introduce evaluate free web-based software application designed take area volume measurements on 3D models patient faces. Methods: employed the online conduct ten subjects, including five volume. These were then compared with those obtained from established modeling called Blender (version 3.2) using Bland–Altman plot. To ensure accuracy, intra-rater inter-rater reliabilities evaluated Intraclass Correlation Coefficient (ICC) method. Additionally, statistical assumptions such as normality homoscedasticity rigorously verified before analysis. Results: found that showed high agreement within 95% confidence limits. Moreover, demonstrated excellent reliability in most analyses, indicated ICC test. Conclusion: findings suggest reliable analysis, particularly measuring volumes. indicates its potential utility enhancing surgical evaluation surgeries. underscores software’s capability improve outcomes integrating precise into surgery assessment processes.

Язык: Английский

Процитировано

1
Innovative Fracture Diagnosis: MobileNet CNN Approach for Precise Bone Fracture Detection and Classification DOI

Khushi Mittal,

Kanwarpartap Singh Gill,

Rahul Chauhan

и другие.

Опубликована: Май 3, 2024

Язык: Английский

Процитировано

1
New Era in Fracture Diagnosis using Deep Learning's Role in Precision Prediction DOI

Arpanpreet Kaur,

Kanwarpartap Singh Gill, Kapil Rajput

и другие.

Опубликована: Авг. 8, 2024

Язык: Английский

Процитировано

0
The future is a door, the past is the key: an essay of the 2024 Mustardé Lecture DOI

Francesco M. Quaranta Leoni

Orbit, Год журнала: 2024, Номер unknown, С. 1 - 9

Опубликована: Окт. 22, 2024

Язык: Английский

Процитировано

0
Advanced CNN Approach for Segmentation of Diseased Areas in Plant Images DOI
Abdullah ŞENER, Burhan Ergen

Deleted Journal, Год журнала: 2024, Номер 76(6), С. 1569 - 1583

Опубликована: Окт. 25, 2024

Early and accurate diagnosis of plant diseases in agriculture is crucial to increase productivity, reduce the use chemicals, save costs obtain high quality products. Conventional methods are time consuming prone human error detecting diseased areas. Therefore, automatic detection regions images obtained from imaging devices becoming increasingly important modern agriculture. In this study, a novel deep learning-based model called Plant Diseased Region Detection Segmentation Network (PDRDSegNet) proposed solve problem segmenting leaves. PDRDSegNet was developed as semantic segmentation specifically optimized for disease detection. The performance compared with common models such UNet, SegNet, FCN8, DeepLabV3+, ENet, PSPNet ICNet. training testing other were performed using "Leaf Disease Dataset," which widely used results show that achieved highest score an mIoU accuracy 86.05%. addition, found achieve higher rates fewer parameters, optimizing computational costs. These indicate can be effective tool

Язык: Английский

Процитировано

0
Fracture Forecasting through Deep Learning's Role in Bone Injury Detection and Classification DOI

Mohit Beri,

Kanwarpartap Singh Gill, Deepak Upadhyay

и другие.

Опубликована: Июль 27, 2024

Язык: Английский

Процитировано

0