Pregled bibliografske jedinice broj: 1165405
Modeling Uncertainty in Fracture Age Estimation from Pediatric Wrist Radiographs
Modeling Uncertainty in Fracture Age Estimation from Pediatric Wrist Radiographs // Mathematics, 9 (2021), 24; 3227, 17 doi:10.3390/math9243227 (međunarodna recenzija, članak, znanstveni)
CROSBI ID: 1165405 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Modeling Uncertainty in Fracture Age Estimation from
Pediatric Wrist Radiographs
Autori
Hržić, Franko ; Janisch, Michael ; Štajduhar, Ivan ; Lerga, Jonatan ; Sorantin, Erich ; Tschauner, Sebastian
Izvornik
Mathematics (2227-7390) 9
(2021), 24;
3227, 17
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
fracture age ; forensic ; deep learning ; uncertainty estimation ; Gaussian process ; X-ray
Sažetak
In clinical practice, fracture age estimation is commonly required, particularly in children with suspected non-accidental injuries. It is usually done by radiologically examining the injured body part and analyzing several indicators of fracture healing such as osteopenia, periosteal reaction, and fracture gap width. However, age-related changes in healing timeframes, inter-individual variabilities in bone density, and significant intra- and inter-operator subjectivity all limit the validity of these radiological clues. To address these issues, for the first time, we suggest an automated neural network-based system for determining the age of a pediatric wrist fracture. In this study, we propose and evaluate a deep learning approach for automatically estimating fracture age. Our dataset included 3570 medical cases with a skewed distribution toward initial consultations. Each medical case includes a lateral and anteroposterior projection of a wrist fracture, as well as patients’ age, and gender. We propose a neural network-based system with Monte-Carlo dropout-based uncertainty estimation to address dataset skewness. Furthermore, this research examines how each component of the system contributes to the final forecast and provides an interpretation of different scenarios in system predictions in terms of their uncertainty. The examination of the proposed systems’ components showed that the feature-fusion of all available data is necessary to obtain good results. Also, proposing uncertainty estimation in the system increased accuracy and F1-score to a final 0.906±0.011 on a given task.
Izvorni jezik
Engleski
Znanstvena područja
Matematika, Računarstvo, Temeljne tehničke znanosti, Temeljne medicinske znanosti
POVEZANOST RADA
Projekti:
NadSve-Sveučilište u Rijeci-uniri-tehnic-18-15 - Razvoj postupaka temeljenih na strojnom učenju za prepoznavanje bolesti i ozljeda iz medicinskih slika (Štajduhar, Ivan, NadSve ) ( CroRIS)
HRZZ-IP-2020-02-3770 - Strojno učenje za prijenos znanja u medicinskoj radiologiji (RadiologyNET) (Štajduhar, Ivan, HRZZ - 2020-02) ( CroRIS)
VLASTITA-SREDSTVA-uniri-tehnic-17 - Računalom potpomognuta digitalna analiza i klasifikacija signala (UNIRI-TEHNIC-18-17) (Lerga, Jonatan, VLASTITA-SREDSTVA - UNIRI2018) ( CroRIS)
Ustanove:
Tehnički fakultet, Rijeka
Citiraj ovu publikaciju:
Časopis indeksira:
- Current Contents Connect (CCC)
- Web of Science Core Collection (WoSCC)
- Science Citation Index Expanded (SCI-EXP)
- SCI-EXP, SSCI i/ili A&HCI
- Scopus