A new study shows how routine blood tests and machine learning can help predict the risk of threatened miscarriage early in pregnancy.
As laboratory diagnostics continue to evolve alongside artificial intelligence (AI) and machine learning (ML), a new study out of China highlights the growing potential of routine blood testing as a predictive tool in obstetric care. Researchers have developed a machine learning model capable of identifying early signs of threatened miscarriage using only standard blood test results.
For lab leaders, this is a key opportunity to use existing lab data and infrastructure to support early pregnancy risk detection. Properly standardized blood data can help shift diagnostics upstream, potentially identifying issues before symptoms appear.
A threatened miscarriage (also called threatened abortion) is a medical condition in early pregnancy where a woman experiences vaginal bleeding before 20 weeks gestation, but the cervix remains closed and the pregnancy is still viable—meaning the fetus is alive with a detectable heartbeat.
To perform the research, scientists from the Second Affiliated Hospital of Shaanxi University of Chinese Medicine, collected medical records and analyzed data from 1,764 patients with threatened miscarriage and 1,489 healthy control subjects. Blood test data of the study participants were collected and a data preprocessing tactic called the Z-score normalization technique, also known as standardization, was applied to routine blood indicators. The high-level, general-purpose programming language called Python was used to facilitate the data transformation of eight different ML algorithms. The performance of those ML models were evaluated by calculating their area under the curve (AUC) values, which represents the overall performance of the various models.
Machine Learning Unlocks Predictive Power of Routine Blood Tests
The ML algorithm known as Deep Neural Network (DNN) achieved the best predictive performance with an AUC value of 96.76% That model also had the top metrics for accuracy at 91.88%, specificity (91.62%) and sensitivity (92.11%).
According to the study, approximately 25% of all pregnancies involve a threatened miscarriage and women who experience this condition are 2.5 times more likely to experience a miscarriage compared to healthy pregnancies.
“We’re not sure why this happens in some pregnancies but not in others,’ said Lisa Jackson, MD, assistant clinical professor in the Raquel and Jaime Gilinski Department of Obstetrics, Gynecology and Reproductive Science at Icahn School of Medicine at Mount Sinai, in an interview with The Bump.
Lisa Jackson, MD, assistant clinical professor in the Raquel and Jaime Gilinski Department of Obstetrics, Gynecology and Reproductive Science at Icahn School of Medicine at Mount Sinai noted, “The good news is that the majority of the time, bleeding in early pregnancy with the presence of fetal heart tones and a closed cervix doesn’t result in miscarriage.” (Photo credit: Icahn School of Medicine at Mount Sinai)
“For most women, the outlook is good, and they go on to have normal pregnancies,” added women’s health expert Jennifer Wider, MD.
Neither Jackson nor Wider were affiliated with the study out of China.
The study links incidents of threatened miscarriage with various factors, including chromosomes, genes, hormonal imbalances, immune factors, and maternal health as well as environmental influences. The researchers also stated in their study, “Women experiencing threatened abortion often encounter significant stress and require multiple clinical assessments to accurately determine their pregnancy status. Threatened abortion not only poses a threat to the health of pregnant women but may also affect their future fertility.”
The researchers believe their ML algorithm has immense clinical value and can assist doctors in accurately identifying high-risk patients. They suggest exploring the application of the DNN model in different clinical settings and patient populations and incorporating more diverse data sources to optimize its predictive capabilities.
A New Role for Clinical Labs in Reproductive Health
The researchers concluded, “Our research on constructing a prediction model for threatened abortion through routine blood tests has revealed the great potential of ML algorithms in detecting threatened abortion. This algorithm is expected to analyze routine blood data to identify at-risk pregnancies at an early stage, significantly improving the early detection of this common pregnancy complication. It will assist healthcare providers in intervening earlier and reducing the incidence of (threatened) abortion.”
More research and trials are needed before any ML model may be used for the early detection of threatened miscarriage incidents. However, this latest study does suggest the potential of machine learning algorithms could be beneficial in timely predictions of the medical condition, which would improve patient outcomes.
For laboratories, this research underscores the untapped value in the routine data already being collected daily. As machine learning models like the deep neural network in this study continue to demonstrate clinical relevance, lab leaders are uniquely positioned to facilitate their integration—by ensuring high-quality, normalized data flows and supporting interdisciplinary collaboration between data scientists and clinicians.
Moving forward, blood testing won’t just confirm conditions, it may soon help predict and prevent them. With strategic investment in data analytics and model validation, labs can transform from diagnostic endpoints into proactive partners in patient care.
New program draws bipartisan criticism and concern from patients and doctors.
Shrewd labs will keep an eye on the latest Centers for Medicare & Medicaid Services (CMS) prior authorization pilot that leans on artificial intelligence (AI) to determine treatment options for Medicare patients. While the Wasteful and Inappropriate Service Reduction Model pilot (WISeR) doesn’t directly mention lab tests, staying on the pulse of this growing trend will keep labs thinking ahead on how to minimize impact on bottom line, paperwork, and workflows when these pilots infiltrate lab testing.
An article from POLITICO reported that CMS will start a pilot version of the program as early as January 2026 in six states including Ohio, Texas, Oklahoma, Ariz., N.J., and Wash. Private AI companies will assist and focus on “services that have been vulnerable to fraud, waste and abuse in the past,” the article noted. The voluntary model is slated to span six years through December 31, 2031, according to the Centers for Disease Control and Prevention (CDC).
Among the types of procedures encumbered by the pilot program are knee arthroscopy for osteoarthritis, skin and tissue substitutions, and electrical nerve stimulator implants, CMS noted. All outpatient and emergency services would currently be excluded, they added, as well as “services that would pose a substantial risk to patients if substantially delayed.”
“All recommendations for non-payment will be determined by appropriately licensed clinicians who will apply standardized, transparent, and evidence-based procedures to their review,” CMS added.
The premise of the pilot is to eliminate wasteful spending, with CMS citing 25% of US healthcare spending falling in this category. “According to the Medicare Payment Advisory Commission Medicare spent up to $5.8 billion in 2022 on unnecessary or inappropriate services with little to no clinical benefit,” their website noted.
A Sour Reception
The pilot program is receiving a less-than-warm welcome from both parties—doctors, and patients alike, Politico noted. “It’s been referred to as the AI death panel. You get more money if you’re that AI tech company if you deny more claims. That is going to lead to people getting hurt,” Greg Landsman (D-Ohio) said during the committee hearing.
Landsman noted in the article from POLITICO that a bipartisan desire to put a halt to the program exists among growing concerns about patient harm coming from the program. Landsman “called for the program to be shut down until an independent review board could be erected to review the liability questions and ensure the AI prior authorization pilot doesn’t harm patients.”
“I’m concerned that this AI model will result in denials of lifesaving care and incentivize companies to restrict care,” Frank Pallone (D-N.J.) and House Energy and Commerce Committee ranking member said at the subcommittee meeting on the use of AI in health care held on Sept. 3.
“We have pretty good evidence that prior authorization as a process itself is fraught, adding that AI’s ability to improve the process for patients remains unproven,” Michelle Mello, Stanford University health law professor and witness at the hearing, said.
Looking Ahead
The involvement of AI in healthcare will only continue, and learning what aspects positively impact healthcare versus cause damage will continue to evolve.
Worth noting, there are already two unrelated lawsuits, against UnitedHealthcare and Cigna, that challenge the safety of AI use to deny patient care, POLITICO noted in the article.
Laboratory leaders should keep their eyes open and their ears to the ground on not only the pilot but all AI healthcare trends.
Findings could reduce the need for self-reporting in future nutritional studies and lead to new clinical laboratory testing
Clinical laboratory testing may one day influence whether a person snacks on a bag of chips every day or chooses to eat healthy foods instead.
Researchers at the National Institutes of Health (NIH) reported that they have identified biomarkers in blood and urine that can indicate an individual’s consumption of ultra-processed foods (UPF).
Scientists discovered a signature that is predictive of a dietary pattern that’s high in ultra-processed food, study leader Erikka Loftfield, PhD, MPH, epidemiologist and principal investigator with the NIH, told the Associated Press (AP).
Using data on the biomarkers—metabolites left after the body breaks down food—the researchers devised a “poly-metabolite score” that could potentially “reduce the reliance on, or complement the use of, self-reported dietary data in large population studies,” according to an NIH press release.
This will be helpful because, according to the AP, “Typical nutrition studies rely on recall: asking people what they ate during a certain period. But such reports are notoriously unreliable because people don’t remember everything they ate, or they record it inaccurately.”
“Limitations of self-reported diet are well known. Metabolomics provides an exciting opportunity to not only improve our methods for objectively measuring complex exposures like diet and intake of ultra-processed foods, but also to understand the mechanisms by which diet might be impacting health,” said Loftfield in the press release.
Thus, it’s conceivable that one day clinical laboratory testing could affect people’s food choices and help to improve their health.
“There’s a need for both a more objective measure and potentially also a more accurate measure,” Erikka Loftfield, PhD, MPH, epidemiologist and principal investigator with the NIH, told the Associated Press. (Photo copyright: National Cancer Institute.)
Study Methodology
The findings were based in part on an earlier study of 718 AARP members, aged 50-74, who agreed to submit blood and urine samples. The participants also completed dietary recall reports.
“The researchers found hundreds of metabolites that correlated with the percentage of energy from ultra-processed foods in the diet,” the NIH press release noted. “Using machine learning, researchers identified metabolic patterns associated with high intake of ultra-processed foods and calculated poly-metabolite scores for blood and urine separately.”
To test their findings, the researchers referred to a 2019 NIH study involving 20 adults aged 18 to 50. Under carefully controlled conditions, these participants spent two weeks consuming high levels of ultra-processed foods, followed by two weeks consuming no ultra-processed foods. As with the AARP cohort, they also submitted blood and urine samples. The poly-metabolite score proved to be an accurate measure of which diets they had consumed, the researchers reported.
The researchers acknowledged limitations in the study that will necessitate further research. “Study participants were older US adults whose diets may vary from other populations,” the authors noted. “Poly-metabolite scores should be evaluated and iteratively improved in populations with diverse diets and a wide range of UPF intake.”
Ultra-Processed Foods Defined
The NIH defines ultra-processed foods as “ready-to-eat or ready-to-heat, industrially manufactured products, typically high in calories and low in essential nutrients.” Diets high in these foods have been associated with “increased risk of obesity and related chronic diseases, including some types of cancer,” the press release noted.
In identifying these foods, the researchers cited a 2019 paper published in the journal Public Health Nutrition (PHN). The paper relied on the NOVA classification system, which makes a distinction between “processed” and “ultra-processed” foods. The latter typically contain “food substances never or rarely used in kitchens,” or cosmetic additives “whose function is to make the final product palatable or more appealing,” the PHN paper noted.
“From sugary cereals at breakfast to frozen pizzas at dinner, plus in-between snacks of potato chips, sodas and ice cream, ultra-processed foods make up about 60% of the US diet,” the AP reported in an earlier story. “For kids and teens, it’s even higher—about two-thirds of what they eat.”
Researchers in Sweden develop urine test that more effectively screens for prostate cancer than standard PSA test
Clinical laboratories may soon have a new inexpensive, non-invasive urine test to screen for prostate cancer that produces superior results compared to the standard PSA test.
An international team of scientists led by researchers at the Karolinska Institutet in Sweden found they could use machine learning to not only accurately identify the presence of a new set cancer biomarkers in urine samples but also determine the stage or grade of the cancer.
“There are many advantages to measuring biomarkers in urine,” said Mikael Benson, principal researcher in the Department of Clinical Science, Intervention and Technology at Karolinska Institutet and senior investigator for the study, in a news release. “It’s non-invasive and painless and can potentially be done at home. The sample can then be analyzed using routine methods in clinical labs.”
“New, more precise biomarkers than PSA can lead to earlier diagnosis and better prognoses for men with prostate cancer,” said Mikael Benson, principal researcher at Karolinska Institutet and senior investigator for the study, in a news release. “Moreover, it can reduce the number of unnecessary prostate biopsies in healthy men.” (Photo copyright: Karolinska Institutet.)
New Prostate Cancer Biomarkers
According to the American Cancer Society, there will be approximately 313,780 new cases of prostate cancer diagnosed this year in the US with about 35,770 deaths due to the disease. About one in eight US men will be diagnosed with prostate cancer in their lifetime, and the lifetime risk of dying from prostate cancer is one in 44 men.
“Early cancer diagnosis is crucial but challenging owing to the lack of reliable biomarkers that can be measured using routine clinical methods. The identification of biomarkers for early detection is complicated by each tumor involving changes in the interactions between thousands of genes. In addition to this staggering complexity, these interactions can vary among patients with the same diagnosis as well as within the same tumor,” the researchers wrote in Cancer Research.
The scientists “hypothesized that reliable biomarkers that can be measured with routine methods could be identified by exploiting three facts:
The same tumor can have multiple grades of malignant transformation;
These grades and their molecular changes can be characterized using spatial transcriptomics; and,
These changes can be integrated into models of malignant transformation using pseudotime models to prioritize the genes that were most correlated with malignant transformation.”
To perform their study, the scientists analyzed the mRNA activity of cells in prostate tumors to construct digital models of prostate cancer. These models were then examined using machine learning, a type of artificial intelligence (AI), to locate specific proteins that could be used as biomarkers.
The researchers evaluated these new biomarkers in urine, blood, and tissue samples from more than 2,000 prostate cancer patients along with a control group. The team’s final calculations found the results of the urine test surpassed the current PSA test traditionally used for diagnosing prostate cancer.
“Prostate cancer can be effectively identified by analyzing the expression of candidate biomarkers in urine,” lead study author Martin Smelik, PhD student at Karolinska Institutet, told Fox News. “This approach outperforms the current blood tests based on PSA, but at the same time keeps the advantages of being non-invasive, painless, and relatively cheap.”
Advancements over Traditional PSA Test
Although the prostate-specific antigen (PSA) test typically used by doctors to diagnose prostate cancer can screen for the disease and monitor its progression, it has limitations.
“While PSA is an incredibly sensitive tool for issues related to the prostate, it is not specific to prostate cancer,” Matthew Abramowitz, MD, associate professor in the Department of Radiation Oncology at the Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, told Fox News. “The techniques proposed in the current study suggest the promise of identifying specific cancer markers in the urine, minimizing some of the specificity concerns associated with PSA.”
“This study highlights the power of machine learning applied to patient data in identifying breakthroughs that can help us diagnose cancer earlier, when our treatments are most effective,” Timothy Showalter, MD, a radiation oncologist at UVA Health in Virginia, told Fox News. “Prostate cancer screening has not seen a transformative advance in decades, and current approaches still rely on the PSA blood test, which is known to have low specificity for clinically significant cancers.”
“Overall, this study demonstrates the diagnostic potential of combining spatial transcriptomics, pseudotime, and machine learning for prostate cancer, which should be further tested in prospective studies,” the researchers wrote.
The Karolinska Institutet team is planning large-scale clinical trials as the next phase of their exploration.
Lab professionals will learn more at the upcoming 30th annual edition of the event
Big changes and challenges are coming for the clinical laboratory anatomic pathology industry, and with them a slew of opportunities for lab and pathology practice leaders. At the upcoming 30th Annual Executive War College on Diagnostics, Pathology, and Clinical Laboratory Management, expert speakers and panelists will focus on the three most disruptive forces.
There will be more than 169 presenters at this year’s Executive War College. Those speakers include:
David Dexter, MD, clinical and laboratory pathology at M Health Fairview, and Sam Terese, president and CEO at Alverno Laboratories, who will present a strategic case study about the support labs can provide to parent hospitals when navigating new waters.
Paul Wilder, executive director of CommonWell Health Alliance, who will speak on the effort to improve the transferability and portability of patient and healthcare data in ways that improve the quality of care.
“Since the inception of The Dark Report in 1995 there has been continual change both within the US healthcare system and within the profession of laboratory medicine,” noted Robert L. Michel, Dark Daily’s editor-in-chief and creator of the Executive War College. “Now, three decades later, the following three items are imperatives for all labs: controlling costs; having adequate lab staff across all positions; and having enough capital to acquire and deploy new diagnostic technologies, along with the latest information technologies.”
“Most clinical laboratory managers would agree that many of the same operational pain points faced by labs in the 1990s exist today,” said Robert L. Michel (above), founder of the Executive War College. In an interview with Dark Daily, Michel broke down the nuances of this triad of forces and what participants in the Executive War College can expect. (Photo copyright: LabX.)
Forces at Work in Clinical Labs and Pathology Groups
Here’s a more detailed look at each of the forces that Michel noted.
Force 1: An acute shortage of experienced lab scientists
“When you look at the supply-demand for laboratory personnel in the United States today, it is recognized that demand exceeds supply, and that gap continues to widen,” Michel noted. “For example, in the case of anatomic pathologists, the increased number of case referrals grows faster than medical schools can train new pathologists. Currently, the ability of pathology laboratories large and small to hire and retain an adequate number of pathologists is a challenge.”
Executive War College attendees can expect panelists and speakers to highlight creative problem solving techniques to circumvent the challenges labor shortages cause.
Force 2: New applications of artificial intelligence
“Today every instrument vendor, every automation supplier, every software supplier, every service supplier is telling labs that they have artificial intelligence (AI) baked inside,” Michel observed. “It is important for lab managers to understand that a variety of technologies are used by different AI solutions.”
Clinical laboratory managers and pathologists interested in acquiring a deeper understanding of where to start with AI in their lab will find numerous sessions on artificial intelligence at this year’s Executive War College. “There will be a number of sessions this year where clinical labs discuss their success deploying various AI solutions,” Michel said.
Force 3: Financial stress across the entire US healthcare system
“It’s recognized that a significant number of US hospitals and integrated delivery networks (IDNs) are struggling to maintain adequate operating margins,” Michel noted. “This obviously impacts the clinical laboratories serving these hospitals. If the hospitals’ cash flows and operating profit margins are being squeezed, typically the administration comes to the lab team and says, ‘Your budget for next year will be x% less than this year.’
“There are many IDNs and hospital labs where budget cuts have happened for multiple years,” Michel continued. “As a consequence, labs in these hospitals must be nimble to maintain a high-quality menu of diagnostic tests. Several years of such budget cuts by the parent hospital can undermine the ability of the clinical lab team to offer competitive salary packages to attract and retain the clinical lab scientists, pathologists, and clinical chemists they need.”
Recognizing Opportunities in Today’s Lab Market
The good news is that—despite the negative forces acting upon the US healthcare system today—clinical laboratories, genetic testing companies, and anatomic pathology groups have a path forward.
“This path forward is informed by two longstanding precepts recognized by innovative managers. One precept is ‘Change creates new winners and losers.’ The other precept is ‘Change creates opportunity,’” Michel said. “Savvy lab leaders recognize the powerful truths in each precept.
“As healthcare has changed over the past four decades, nearly all the regional and national laboratories that were dominant in 1990, for example, don’t exist today!” he noted. “And yet, even as these lab organizations disappeared, new clinical lab organizations emerged that recognized healthcare’s changes and organized themselves to serve the changing needs of hospitals, office-based physicians, payers, and patients.”
All of these critical topics and more will be covered during the 30th Annual Executive War College on Diagnostics, Clinical Laboratory, and Pathology Management on April 29-30, 2025, at the Hyatt Regency in New Orleans. Signup today to bring your lab’s management team by registering at https://www.executivewarcollege.com.
