2026年10月21日 (英语).
Angelica Willis,Google研究研究工程师和Nichole Young-Lin，临床铅，Google促进健康
一项可能的多中心研究显示，基于AI的盲扫超声波评估如何能够使具有低成本探测器的非声学操作者提供专家一级的妊娠年龄评估，标志着在全球范围扩大拯救生命的孕产妇护理迈出了重要一步。
世界卫生组织(世卫组织)估计，每年全世界因怀孕并发症而导致的可预防死亡超过25万人。 虽然产前超声波可以改善结果，降低胎儿和产妇死亡率，但全球三分之二的人无法获得超声波等诊断医学成像。 目前支持产妇保健的方法严重依赖专业临床培训，使数百万人处于资源匮乏的环境中，在整个怀孕期间无法接受一次超声波检查。
我们看到了一个研究机会，通过开发解释“盲扫”超声波的机器学习系统来解决这种现实世界的需要。 这种扫描方式使得没有专门的声学训练的前线保健工作者能够进行扫描，只需以标准化的方式在腹部对面滑翔低成本的便携式超声波探测器即可进行扫描。
由于许多区域面临专家严重短缺的问题，传统的配音员培训既昂贵又需要多年承诺，使这些保健工作者能够在极少培训的情况下进行评估，有可能大大扩大产前检查的覆盖面。
将最低限度的培训要求与人工智能解释结合起来，最终可能意味着一个成功的开发工具，它有可能增加基于超声波的妊娠评估，从而改善获得高质量产妇保健的机会。
在JAMA网络公开发表的“利用盲扫超声波学对基于AI的胚胎年龄评估的概括化”中，我们评价了人工智能的孕龄评估模型(怀孕期间有多远)在不同的临床环境和硬件中的普遍化程度。 虽然我们之前的研究验证了模型对来自同一追溯人群的隐形患者的性能，但这项新的未来研究显示如何在真正的诊所的移动设备上可靠地运行AI系统，只有新手和不熟悉的超声波设备，以及最小的微调。
“怀孕年龄基本上是指妇女怀孕的时间。 我们所有的检查，我们的许多治疗，我们很多的送货迹象，都是基于你从孕期得到的这个总时间表。”
医疗AI系统要想在全球安全分布，就必须表现出通俗性——当推出到不同患者群的新临床环境，临床工作流程，超声波硬件变异时，它必须保持准确的诊断性能，而超声波硬件变异则要比模型所训练的要多。
在这项研究中，我们最初的人工智能模型只针对我们的目标人群之一，然后在两个目标环境中进行预期测试：
- 西北医学：位于美国伊利诺伊州芝加哥的一座城市高资源学术医学中心。
- Jacaranda保健：肯尼亚内罗毕的一个城市社区诊所网络。
最初在北卡罗莱纳州和赞比亚使用Sonosite,GE，和蝴蝶网络商用超声波探测器进行超声波数据培训，研究AI系统与Clarius C3对齐，这是一种商用手持式横腹探测器。 所有这些场所在各种护理方面各不相同，包括妇女首次来接受产前护理时、她们接受多少常规超声波检查以及孕期通常如何。
为了评价高效的系统调试，该研究利用了由来自芝加哥的120名参与者组成的小型试点数据集——大约占最初培训组合的6%——使模型适应新的探测硬件，并将孕期预测范围延长到36周以上。 为了理解通用性，没有使用来自内罗毕站点的数据进行微调，维持肯尼亚临床验证为严格独立的外部测试。 为了评价全面的效用，该研究还包括多次怀孕，这可能对人类操作人员和自动化系统构成跟踪和测量挑战。
我们比较了人工智能系统从盲扫超声波估计胎儿年龄的能力与临床标准专家方法的公式估计。 这种专家方法依靠一位专家的声学学家来定位胎儿的多个特定截面，以进行精确的生物测量，这是一项难以衡量的任务，需要多年的培训，并且高度依赖操作人员。
对怀孕16周至36周的385名初级学员进行了评估，诊断研究表明，人工智能系统取得了强有力的成果：
- 临床上非急性精度：在两个组别中，人工智能模型在估计妊娠年龄时得出了4.18天的平均绝对误差，成功地证明专家声学专家对标准临床程序无过敏性，其平均误差为4.48天。
- 交叉设定概括：在芝加哥，经过改造的模型实现了4.10天的MAE. 在内罗毕，通过零局部优化，该模型实现了可比的4.27天的MAE.
除了准确性外，探索性衡量标准表明，研究AI系统在复杂的情景中具有弹性。 例如，在胎儿体重极端(高于90%)的情况下，模型的性能超过了传统的生物测量公式，显示出与胎儿大小变化相比的强性。
除了强调通过AI扩展超声波访问的巨大机会外，未来的多中心研究还使我们能够积极观察和优化AI在现场临床环境中的行为。 通过将我们的系统直接应用到日常的临床工作流程中，我们获得了实用的洞察力，帮助我们设计出真实世界的用途。 例如，我们观察到，在接连病人之间保持充电装置的插座停靠站上，必须互换电池。
这些研究还表明，采用这种人工智能系统还需要与临床医生和病人建立深刻的信任。 最终实现这一目标的一个重要方法就是在应用中建立明确的保障措施，比如我们的模型为拒绝质量差的扫描并向临床用户传达模型不确定性而提出的模型信任估计。
这些研究结果表明，基于AI的系统可以分析没有经过专业的声学训练的操作者标准化的“盲扫”图像收购。 人工智能对胎儿年龄的估计准确性与专家操作人员目前的护理标准相当，更昂贵的传统的、以推车为基础的超声波机器。 展示不同临床环境、硬件和不断变化的人口在技术上的可通用性，标志着在强调他们扩大孕产妇保健这一关键能力的潜力方面迈出了关键一步。
这项研究赋予了受过最低培训的社区保健工作者能力，使其能够进行可靠的超声波学评估，表明有可能在资源不足地区扩大数百万人的产前覆盖率。 研究这种可扩展技术的能力，为建立更具有复原力、更方便的保健系统奠定了基础，确保每一个母亲，无论身处何处，都有机会进行健康的怀孕和安全分娩。
这项护理点研究有可能运用我们通过大赦国际扩大获得保健服务的更广泛愿景。 从这项研究中获得的深刻见解为我们下一代多模式医疗情报研究的发展提供了信息。 通过我们的持续工作，我们努力向发展界和科学家提供他们需要的答案，以改善临床护理，并使临床护理对所有人都有帮助和方便。
感谢我们跨越Google研究、贾卡兰达保健和西北大学费恩伯格医学院的合著者：Chace Lee、Justin Krogue、Anneka Wickramanayake、Stacey Caron、Priscah Cheruiyot、Amber Watters、Alicia Martín、Tiya Tiyasirichokchai、Akib Udddin、Yun Liu、Patricia Strachan、Sunny Jansensen、Alyssa Santi、Preeti Singh、Catherine Arguelles、Kenneth Hudson、David Melnick、Mahesh Vaidyanathan、Moziyar Etemadi、Yossi Matias、Avinatan Hassidim、Greg Corrado、Shruthi Prabakhakara、Daniel Golden、Ryan G. Gomes和Shravya Shetty。 感谢皮春昌和戴尔·韦伯斯特对本作的评论。
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October 21, 2026
Angelica Willis, Research Engineer, Google Research, and Nichole Young-Lin, Clinical Lead, Google for Health
A prospective multi-center study demonstrates how AI-based blind sweep ultrasound assessment can enable non-sonographer operators with low-cost probes to deliver expert-level gestational age assessments, marking a significant step toward expanding life-saving maternal care globally.
The World Health Organization (WHO) estimates that more than 250,000 preventable deaths occur worldwide each year due to pregnancy-related complications. While prenatal ultrasound can improve outcomes and reduce fetal and maternal mortality, two-thirds of people globally lack access to diagnostic medical imaging like ultrasound. Current methods that support maternal health rely heavily on specialized clinical training, leaving millions in low-resource settings without access to a single ultrasound during their entire pregnancy.
We saw an opportunity to conduct research towards addressing this real-world need, by developing machine learning systems that interpret "blind sweep" ultrasounds. This way of scanning allows frontline healthcare workers without specialized sonography training to perform scans by simply gliding low-cost, portable ultrasound probes across the abdomen in a standardized pattern.
Because many regions face critical shortages of experts, and traditional sonographer training is both expensive and requires a multi-year commitment, enabling these health workers to perform assessments with minimal training has the potential to significantly expand prenatal screening coverage.
Combining the minimal training requirement with AI interpretations could ultimately mean a successfully developed tool that has the potential to increase availability of ultrasound-based pregnancy assessment, and thereby improve access to higher quality maternal health.
In “Generalization of AI-Based Gestational Age Assessment Using Blind Sweep Ultrasonography”, published in JAMA Network Open, we evaluated how effectively an AI model for gestational age assessment (how far along the pregnancy is) generalizes across diverse clinical settings and hardware. While our previous research validated the model's performance on unseen patients from the same retrospective cohort it learned from, this new prospective study shows how the AI system performs reliably running live on mobile devices in real clinics, with only novice operators and unfamiliar ultrasound equipment, and minimal fine-tuning.
“The gestational age is basically how far along a woman is in pregnancy. All of our screening, a lot of our treatments, a lot of our indications for delivery are all based on this master timeline that you get from the gestational age.”
For a medical AI system to safely scale globally, it must demonstrate generalizability — maintaining accurate diagnostic performance when introduced to new clinical settings with different patient populations, clinical workflows, and ultrasound hardware variations than those on which the model was trained.
In this study, our original AI model was minimally tuned on only one of our target populations, and then prospectively tested across both target environments:
- Northwestern Medicine: An urban, high-resource academic medical center in Chicago, IL, United States.
- Jacaranda Health: An urban community clinic network in Nairobi, Kenya.
Originally trained on ultrasound data collected in North Carolina and Zambia using a set of Sonosite, GE, and Butterfly Networks commercial ultrasound probes, the research AI system was paired with Clarius C3, a commercial handheld transabdominal probe. All of these sites differed in various care dimensions, including when women first come in for prenatal care, how many routine ultrasounds they receive, and how gestational age is usually dated.
To evaluate efficient system tuning, the study leveraged a small pilot dataset of 120 participants from Chicago — approximately 6% of the original training set—to adapt the model to new probe hardware and extend the gestational age prediction range beyond 36 weeks. To understand generalizability, no data from the Nairobi site was used for fine-tuning, maintaining the Kenyan clinical validation as a strictly independent, external test. To evaluate comprehensive utility, the study also included multiple gestation pregnancies, which could present tracking and measurement challenges for both human operators and automated systems.
We compared the AI system’s ability to estimate fetal age from blind sweep ultrasounds to formula-based estimation from the clinically standard expert approach. This expert approach relies on an expert sonographer locating multiple specific cross-sections of the fetus(es) to take precise biometry measurements, a difficult-to-scale task that requires years of training and is highly operator-dependent.
Evaluating a primary cohort of 385 participants between 16 and 36 weeks of pregnancy, the diagnostic study demonstrated strong AI system results:
- Clinically non-inferior accuracy: Across both cohorts, the AI model achieved a mean absolute error (MAE) of 4.18 days in estimating gestational age, successfully demonstrating non-inferiority to the standard clinical procedure performed by expert sonographers, which had an MAE of 4.48 days.
- Cross-setting generalization: In Chicago, the adapted model achieved an MAE of 4.10 days. In Nairobi, with zero local optimization, the model achieved a comparable MAE of 4.27 days.
Beyond accuracy, exploratory metrics showed that the research AI system was resilient in complex scenarios. For instance, in cases of fetal weight extremes (above the 90th percentile), the model outperformed traditional biometry formulas, demonstrating robustness against variations in fetal size.
In addition to highlighting an immense opportunity to expand ultrasound access through AI, the prospective multi-center study allowed us to actively observe and optimize how AI behaves in live clinical environments. By deploying our system directly into everyday clinical workflows, we gained practical insights that help us design for real world use. For example, we observed the importance of swappable batteries over plug-in docking stations for keeping devices charged between consecutive patients.
These studies also demonstrated how deploying AI systems like this will also require building deep trust with both clinicians and patients. One important way to ultimately achieve this would be by building clear safeguards into the app, like model confidence estimates produced by our model to reject poor-quality sweeps and convey model uncertainty to the clinician user.
These study findings indicate that AI-based systems can analyze standardized “blind sweeps” images acquired by operators without specialized sonography training. The AI estimate of fetal age had accuracy comparable to the current standard of care from expert operators and more expensive traditional, cart-based ultrasound machines. Demonstrating technical generalizability across diverse clinical settings, hardware, and changing populations, marks a key step in highlighting their potential to expand access to this critical capability for maternal health.
By empowering minimally trained community health workers to perform reliable ultrasonography assessments, this research demonstrates the potential to expand prenatal coverage for millions in under-resourced areas. Studying the capabilities of this scalable technology lays the groundwork for more resilient, accessible healthcare systems, ensuring that every mother, regardless of location, has the chance for a healthy pregnancy and a safe delivery.
This point-of-care research represents a potential application of our broader vision for expanding healthcare accessibility through AI. The insights gained from this research inform the development of our next-generation, multimodal medical intelligence research. Through our continued work, we strive to provide the development community and scientists with the answers they need to improve clinical care and make it helpful and accessible to all.
Thank you to our co-authors across Google Research, Jacaranda Health and Northwestern University Feinberg School of Medicine: Chace Lee, Justin Krogue, Anneka Wickramanayake, Stacey Caron, Priscah Cheruiyot, Amber Watters, Alicia Martín, Tiya Tiyasirichokchai, Akib Uddin, Yun Liu, Patricia Strachan, Sunny Jansen, Alyssa Santi, Preeti Singh, Catherine Arguelles, Kenneth Hudson, David Melnick, Mahesh Vaidyanathan, Mozziyar Etemadi, Yossi Matias, Avinatan Hassidim, Greg Corrado, Shruthi Prabhakara, Daniel Golden, Ryan G. Gomes, and Shravya Shetty. Thank you to Pi-Chuan Chang and Dale Webster for reviewing this work.
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