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Assessing accuracy and consistency in intracranial aneurysm sizing : human expertise vs. artifcial intelligence
ID
Planinc, Andrej
(
Avtor
),
ID
Špegel, Nina
(
Avtor
),
ID
Podobnik, Zala
(
Avtor
),
ID
Šinigoj, Uroš
(
Avtor
),
ID
Skubic, Petra
(
Avtor
),
ID
Choi, June Ho
(
Avtor
),
ID
Park, Wonhyoung
(
Avtor
),
ID
Robič, Tina
(
Avtor
),
ID
Tabor, Nika
(
Avtor
),
ID
Jarabek, Leon
(
Avtor
),
ID
Špiclin, Žiga
(
Avtor
),
ID
Bizjak, Žiga
(
Avtor
)
PDF - Predstavitvena datoteka,
prenos
(1,91 MB)
MD5: 86071F2CA6E8A6E718825F34521BD901
URL - Izvorni URL, za dostop obiščite
https://www.nature.com/articles/s41598-024-65825-4
Galerija slik
Izvleček
Intracranial aneurysms (IAs) are a common vascular pathology and are associated with a risk of rupture, which is often fatal. Aneurysm growth of more than 1 mm is considered a surrogate of rupture risk, therefore, this study presents a comprehensive analysis of intracranial aneurysm measurements utilizing a dataset comprising 358 IA from 248 computed tomography angiography (CTA) scans measured by four junior raters and one senior rater. The study explores the variability in sizing assessments by employing both human raters and an Artificial Intelligence (AI) system. Our findings reveal substantial inter- and intra-rater variability among junior raters, contrasting with the lower intra-rater variability observed in the senior rater. Standard deviations of all raters were above the threshold for IA growth (1 mm). Additionally, the study identifies a systemic bias, indicating a tendency for human experts to measure aneurysms smaller than the AI system. Our findings emphasize the challenges in human assessment while also showcasing the capacity of AI technology to improve the precision and reliability of intracranial aneurysm assessments, especially beneficial for junior raters. The potential of AI was particularly evident in the task of monitoring IA at various intervals, where the AI-based approach surpassed junior raters and achieved performance comparable to senior raters.
Jezik:
Angleški jezik
Ključne besede:
intrarater variability
,
interrater variability
,
deep learning
,
intracranial aneurysms
Vrsta gradiva:
Članek v reviji
Tipologija:
1.01 - Izvirni znanstveni članek
Organizacija:
FE - Fakulteta za elektrotehniko
MF - Medicinska fakulteta
Status publikacije:
Objavljeno
Različica publikacije:
Objavljena publikacija
Založnik:
Springer Nature
Leto izida:
2024
Št. strani:
11 str.
Številčenje:
14, art. 16080
PID:
20.500.12556/RUL-160094
UDK:
004.8:616-007.64
ISSN pri članku:
2045-2322
DOI:
10.1038/s41598-024-65825-4
COBISS.SI-ID:
204579587
Datum objave v RUL:
20.08.2024
Število ogledov:
45
Število prenosov:
0
Metapodatki:
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Objavi na:
Gradivo je del revije
Naslov:
Scientific reports
Skrajšan naslov:
Sci. rep.
Založnik:
Nature Publishing Group
ISSN:
2045-2322
COBISS.SI-ID:
18727432
Licence
Licenca:
CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
Povezava:
http://creativecommons.org/licenses/by/4.0/deed.sl
Opis:
To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
Sekundarni jezik
Jezik:
Slovenski jezik
Ključne besede:
variabilnost ocenjevalcev
,
globoko učenje
,
intrakranialne anevrizme
Projekti
Financer:
ARIS - Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije
Številka projekta:
J2-3059
Naslov:
Sprotno prilagajanje načrta protonske in radioterapije
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