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

Mohit Beri,

Kanwarpartap Singh Gill, Deepak Upadhyay

et al.

Published: July 27, 2024

Language: Английский

Dermatology and artificial intelligence DOI
W Clark Lambert, Andrzej Grzybowski

Clinics in Dermatology, Journal Year: 2024, Volume and Issue: 42(3), P. 207 - 209

Published: Jan. 4, 2024

Language: Английский

Citations

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

Roberto Langella

et al.

Pharmaceutical Research, Journal Year: 2024, Volume and Issue: 41(4), P. 721 - 730

Published: March 5, 2024

Language: Английский

Citations

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

et al.

Ophthalmology Science, Journal Year: 2024, Volume and Issue: 4(5), P. 100518 - 100518

Published: March 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.

Language: Английский

Citations

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

Balázs Fazekas,

Malik Moledina,

Nehal Singhania

et al.

Cureus, Journal Year: 2025, Volume and Issue: unknown

Published: March 27, 2025

Language: Английский

Citations

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

et al.

BioMedInformatics, Journal Year: 2024, Volume and Issue: 4(1), P. 690 - 708

Published: March 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.

Language: Английский

Citations

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

Khushi Mittal,

Kanwarpartap Singh Gill,

Rahul Chauhan

et al.

Published: May 3, 2024

Language: Английский

Citations

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

Arpanpreet Kaur,

Kanwarpartap Singh Gill, Kapil Rajput

et al.

Published: Aug. 8, 2024

Language: Английский

Citations

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

Francesco M. Quaranta Leoni

Orbit, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 9

Published: Oct. 22, 2024

Language: Английский

Citations

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

Deleted Journal, Journal Year: 2024, Volume and Issue: 76(6), P. 1569 - 1583

Published: Oct. 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

Language: Английский

Citations

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

Mohit Beri,

Kanwarpartap Singh Gill, Deepak Upadhyay

et al.

Published: July 27, 2024

Language: Английский

Citations

0