AI in Medical Physics Gidelines for publication

被引：31

作者：

El Naqa, Issam ^{[1
]}

Boone, John M. ^{[2
]}

Benedict, Stanley H. ^{[3
]}

Goodsitt, Mitchell M. ^{[4
]}

Chan, Heang-Ping ^{[4
]}

Drukker, Karen ^{[5
]}

Hadjiiski, Lubomir ^{[4
]}

Ruan, Dan ^{[6
]}

Sahiner, Berkman ^{[7
]}

机构：

[1] H Lee Moffitt Canc Ctr & Res Inst, Machine Learning & Radiat Oncol, 12902 Magnolia Dr, Tampa, FL 33612 USA

[2] Univ Calif Davis Hlth, Dept Radiol, Sacramento, CA USA

[3] Univ Calif Davis Hlth, Radiat Oncol, Sacramento, CA USA

[4] Univ Michigan, Dept Radiol, Ann Arbor, MI 48109 USA

[5] Univ Chicago, Dept Radiol, Chicago, IL 60637 USA

[6] Univ Calif Los Angeles, Sch Med, Radiat Oncol, Los Angeles, CA USA

[7] US FDA, Silver Spring, MD USA

来源：

MEDICAL PHYSICS | 2021年 / 48卷 / 09期

关键词：

D O I：

10.1002/mp.15170

中图分类号：

R8 [特种医学]; R445 [影像诊断学];

学科分类号：

1002 ; 100207 ; 1009 ;

摘要：

The Abstract is intended to provide a concise summary of the study and its scientific findings. For AI/ML applications in medical physics, a problem statement and rationale for utilizing these algorithms are necessary while highlighting the novelty of the approach. A brief numerical description of how the data are partitioned into subsets for training of the AI/ML algorithm, validation (including tuning of parameters), and independent testing of algorithm performance is required. This is to be followed by a summary of the results and statistical metrics that quantify the performance of the AI/ML algorithm.

引用

页码：4711 / 4714

页数：4

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