Dating the skull from Broken Hill, Zambia, and its position in human evolution.

The cranium from Broken Hill (Kabwe) was recovered from cave deposits in 1921, during metal ore mining in what is now Zambia1. It is one of the best-preserved skulls of a fossil hominin, and was initially designated as the type specimen of Homo rhodesiensis, but recently it has often been included in the taxon Homo heidelbergensis2-4. However, the original site has since been completely quarried away, and-although the cranium is often estimated to be around 500 thousand years old5-7-its unsystematic recovery impedes its accurate dating and placement in human evolution. Here we carried out analyses directly on the skull and found a best age estimate of 299 ± 25 thousand years (mean ± 2σ). The result suggests that later Middle Pleistocene Africa contained multiple contemporaneous hominin lineages (that is, Homo sapiens8,9, H. heidelbergensis/H. rhodesiensis and Homo naledi10,11), similar to Eurasia, where Homo neanderthalensis, the Denisovans, Homo floresiensis, Homo luzonensis and perhaps also Homo heidelbergensis and Homo erectus12 were found contemporaneously. The age estimate also raises further questions about the mode of evolution of H. sapiens in Africa and whether H. heidelbergensis/H. rhodesiensis was a direct ancestor of our species13,14.

Blue Turns to Gray: Paleogenomic Insights into the Evolutionary History and Extinction of the Blue Antelope (Hippotragus leucophaeus).

The blue antelope (Hippotragus leucophaeus) is the only large African mammal species to have become extinct in historical times, yet no nuclear genomic information is available for this species. A recent study showed that many alleged blue antelope museum specimens are either roan (Hippotragus equinus) or sable (Hippotragus niger) antelopes, further reducing the possibilities for obtaining genomic information for this extinct species. While the blue antelope has a rich fossil record from South Africa, climatic conditions in the region are generally unfavorable to the preservation of ancient DNA. Nevertheless, we recovered two blue antelope draft genomes, one at 3.4× mean coverage from a historical specimen (∼200 years old) and one at 2.1× mean coverage from a fossil specimen dating to 9,800-9,300 cal years BP, making it currently the oldest paleogenome from Africa. Phylogenomic analyses show that blue and sable antelope are sister species, confirming previous mitogenomic results, and demonstrate ancient gene flow from roan into blue antelope. We show that blue antelope genomic diversity was much lower than in roan and sable antelope, indicative of a low population size since at least the early Holocene. This supports observations from the fossil record documenting major decreases in the abundance of blue antelope after the Pleistocene-Holocene transition. Finally, the persistence of this species throughout the Holocene despite low population size suggests that colonial-era human impact was likely the decisive factor in the blue antelope's extinction.

Radiology 2040.

A translation of this article in Spanish is available in the supplement. Una traducción de este artículo en español está disponible en el suplemento.

Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How?

As the role of artificial intelligence (AI) in clinical practice evolves, governance structures oversee the implementation, maintenance, and monitoring of clinical AI algorithms to enhance quality, manage resources, and ensure patient safety. In this article, a framework is established for the infrastructure required for clinical AI implementation and presents a road map for governance. The road map answers four key questions: Who decides which tools to implement? What factors should be considered when assessing an application for implementation? How should applications be implemented in clinical practice? Finally, how should tools be monitored and maintained after clinical implementation? Among the many challenges for the implementation of AI in clinical practice, devising flexible governance structures that can quickly adapt to a changing environment will be essential to ensure quality patient care and practice improvement objectives.

Medical imaging and nuclear medicine: a Lancet Oncology Commission.

The diagnosis and treatment of patients with cancer requires access to imaging to ensure accurate management decisions and optimal outcomes. Our global assessment of imaging and nuclear medicine resources identified substantial shortages in equipment and workforce, particularly in low-income and middle-income countries (LMICs). A microsimulation model of 11 cancers showed that the scale-up of imaging would avert 3·2% (2·46 million) of all 76·0 million deaths caused by the modelled cancers worldwide between 2020 and 2030, saving 54·92 million life-years. A comprehensive scale-up of imaging, treatment, and care quality would avert 9·55 million (12·5%) of all cancer deaths caused by the modelled cancers worldwide, saving 232·30 million life-years. Scale-up of imaging would cost US$6·84 billion in 2020-30 but yield lifetime productivity gains of $1·23 trillion worldwide, a net return of $179·19 per $1 invested. Combining the scale-up of imaging, treatment, and quality of care would provide a net benefit of $2·66 trillion and a net return of $12·43 per $1 invested. With the use of a conservative approach regarding human capital, the scale-up of imaging alone would provide a net benefit of $209·46 billion and net return of $31·61 per $1 invested. With comprehensive scale-up, the worldwide net benefit using the human capital approach is $340·42 billion and the return per dollar invested is $2·46. These improved health and economic outcomes hold true across all geographical regions. We propose actions and investments that would enhance access to imaging equipment, workforce capacity, digital technology, radiopharmaceuticals, and research and training programmes in LMICs, to produce massive health and economic benefits and reduce the burden of cancer globally.

Radiology in the Era of Value-based Healthcare: A Multi-Society Expert Statement from the ACR, CAR, ESR, IS3R, RANZCR, and RSNA.

Background The Value-Based Healthcare (VBH) concept is designed to improve individual healthcare outcomes without increasing expenditure, and is increasingly being used to determine resourcing of and reimbursement for medical services. Radiology is a major contributor to patient and societal healthcare at many levels. Despite this, some VBH models do not acknowledge radiology's central role; this may have future negative consequences for resource allocation. Methods, findings and interpretation This multi-society paper, representing the views of Radiology Societies in Europe, the USA, Canada, Australia, and New Zealand, describes the place of radiology in VBH models and the health-care value contributions of radiology. Potential steps to objectify and quantify the value contributed by radiology to healthcare are outlined. Published under a CC BY 4.0 license.

Radiology in the Era of Value-Based Healthcare: A Multi-Society Expert Statement From the ACR, CAR, ESR, IS3R, RANZCR, and RSNA.

BACKGROUND: The Value-Based Healthcare (VBH) concept is designed to improve individual healthcare outcomes without increasing expenditure, and is increasingly being used to determine resourcing of and reimbursement for medical services. Radiology is a major contributor to patient and societal healthcare at many levels. Despite this, some VBH models do not acknowledge radiology's central role; this may have future negative consequences for resource allocation. METHODS, FINDINGS AND INTERPRETATION: This multi-society paper, representing the views of Radiology Societies in Europe, the USA, Canada, Australia, and New Zealand, describes the place of radiology in VBH models and the health-care value contributions of radiology. Potential steps to objectify and quantify the value contributed by radiology to healthcare are outlined.

Continuous Learning AI in Radiology: Implementation Principles and Early Applications.

Artificial intelligence (AI) is becoming increasingly present in radiology and health care. This expansion is driven by the principal AI strengths: automation, accuracy, and objectivity. However, as radiology AI matures to become fully integrated into the daily radiology routine, it needs to go beyond replicating static models, toward discovering new knowledge from the data and environments around it. Continuous learning AI presents the next substantial step in this direction and brings a new set of opportunities and challenges. Herein, the authors discuss the main concepts and requirements for implementing continuous AI in radiology and illustrate them with examples from emerging applications.

What Patients Want to Know about Imaging Examinations: A Multiinstitutional U.S. Survey in Adult and Pediatric Teaching Hospitals on Patient Preferences for Receiving Information before Radiologic Examinations.

Purpose To identify what information patients and parents or caregivers found useful before an imaging examination, from whom they preferred to receive information, and how those preferences related to patient-specific variables including demographics and prior radiologic examinations. Materials and Methods A 24-item survey was distributed at three pediatric and three adult hospitals between January and May 2015. The χ2 or Fisher exact test (categorical variables) and one-way analysis of variance or two-sample t test (continuous variables) were used for comparisons. Multivariate logistic regression was used to determine associations between responses and demographics. Results Of 1742 surveys, 1542 (89%) were returned (381 partial, 1161 completed). Mean respondent age was 46.2 years ± 16.8 (standard deviation), with respondents more frequently female (1025 of 1506, 68%) and Caucasian (1132 of 1504, 75%). Overall, 78% (1117 of 1438) reported receiving information about their examination most commonly from the ordering provider (824 of 1292, 64%), who was also the most preferred source (1005 of 1388, 72%). Scheduled magnetic resonance (MR) imaging or nuclear medicine examinations (P < .001 vs other examination types) and increasing education (P = .008) were associated with higher rates of receiving information. Half of respondents (757 of 1452, 52%) sought information themselves. The highest importance scores for pre-examination information (Likert scale ≥4) was most frequently assigned to information on examination preparation and least frequently assigned to whether an alternative radiation-free examination could be used (74% vs 54%; P < .001). Conclusion Delivery of pre-examination information for radiologic examinations is suboptimal, with half of all patients and caregivers seeking information on their own. Ordering providers are the predominant and preferred source of examination-related information, with respondents placing highest importance on information related to examination preparation. © RSNA, 2018 Online supplemental material is available for this article.

Current Applications and Future Impact of Machine Learning in Radiology.

Recent advances and future perspectives of machine learning techniques offer promising applications in medical imaging. Machine learning has the potential to improve different steps of the radiology workflow including order scheduling and triage, clinical decision support systems, detection and interpretation of findings, postprocessing and dose estimation, examination quality control, and radiology reporting. In this article, the authors review examples of current applications of machine learning and artificial intelligence techniques in diagnostic radiology. In addition, the future impact and natural extension of these techniques in radiology practice are discussed.

Bits and bytes: the future of radiology lies in informatics and information technology.

Advances in informatics and information technology are sure to alter the practice of medical imaging and image-guided therapies substantially over the next decade. Each element of the imaging continuum will be affected by substantial increases in computing capacity coincident with the seamless integration of digital technology into our society at large. This article focuses primarily on areas where this IT transformation is likely to have a profound effect on the practice of radiology. KEY POINTS: • Clinical decision support ensures consistent and appropriate resource utilization. • Big data enables correlation of health information across multiple domains. • Data mining advances the quality of medical decision-making. • Business analytics allow radiologists to maximize the benefits of imaging resources.

Critical access hospital ED to quaternary medical center: successful implementation of an integrated Picture Archiving and Communications System for patient transfers by air and sea.

PURPOSE: The purpose of this study was to investigate the role of imaging in transfers between an island Critical Access Hospital (CAH) emergency department (ED) and a quaternary care hospital. METHODS: Electronic medical records were reviewed to identify all patients who were transferred from an island CAH to our quaternary care hospital in 2012 and 2013. Medical history, transfer diagnosis, and the type of imaging performed at the CAH prior to transfer were reviewed. RESULTS: During the study period, a total of 22075 ED visits were made to the CAH and 696 (3.2%) of these patients were transferred for higher level of care, with 424 (60.9%) of the patients transferred to our quaternary care hospital. The most common reasons for transfer were cardiac (121; 28.5%), trauma (82; 19.3%), gastrointestinal (63; 14.9%), and neurologic conditions (54; 12.7%). 349 patients (82.3%) had imaging prior to transfer (56.4% radiograph, 33.5% computed tomography, 4.7% magnetic resonance imaging, 8.0% ultrasound). Of patients that had imaging, 53.6% had positive imaging findings related to the transfer diagnosis, and patients transferred for noncardiac etiologies were significantly more likely to have imaging findings related to their transfer diagnosis compared with patients transferred for cardiac etiologies (72.9% vs 6.9%, respectively; P< .0001). CONCLUSION: Approximately 3 of every 100 ED visits to the rural CAH required transfer for higher level of care, with nearly three-quarters of noncardiac transferred patients having a positive imaging finding related to the reason for transfer.

Microstratigraphic evidence of in situ fire in the Acheulean strata of Wonderwerk Cave, Northern Cape province, South Africa.

The ability to control fire was a crucial turning point in human evolution, but the question when hominins first developed this ability still remains. Here we show that micromorphological and Fourier transform infrared microspectroscopy (mFTIR) analyses of intact sediments at the site of Wonderwerk Cave, Northern Cape province, South Africa, provide unambiguous evidence--in the form of burned bone and ashed plant remains--that burning took place in the cave during the early Acheulean occupation, approximately 1.0 Ma. To the best of our knowledge, this is the earliest secure evidence for burning in an archaeological context.

Applications of justification and optimization in medical imaging: examples of clinical guidance for computed tomography use in emergency medicine.

Availability, reliability, and technical improvements have led to continued expansion of computed tomography (CT) imaging. During a CT scan, there is substantially more exposure to ionizing radiation than with conventional radiography. This has led to questions and critical conclusions about whether the continuous growth of CT scans should be subjected to review and potentially restraints or, at a minimum, closer investigation. This is particularly pertinent to populations in emergency departments, such as children and patients who receive repeated CT scans for benign diagnoses. During the last several decades, among national medical specialty organizations, the American College of Emergency Physicians and the American College of Radiology have each formed membership working groups to consider value, access, and expedience and to promote broad acceptance of CT protocols and procedures within their disciplines. Those efforts have had positive effects on the use criteria for CT by other physician groups, health insurance carriers, regulators, and legislators.

Curbing potential radiation-induced cancer risks in oncologic imaging: perspectives from the 'image gently' and 'image wisely' campaigns.

Medical imaging that uses ionizing radiation, such as CT, radiography, nuclear medicine, and fluoroscopy, is a cornerstone of the care of oncology patients and provides great benefit. Ionizing radiation at high doses is a known carcinogen.The exact degree of the risk of carcinogenesis from the lower doses of ionizing radiation used in medical imaging is less clear. The purpose of this review is to provide the oncology community with knowledge about the doses used in medical imaging, radiation-induced cancer risks from imaging, considerations to keep in mind when balancing imaging benefits and risks in pediatric and adult oncologic settings, dose reduction strategies, and the "Image Gently" and "Image Wisely" campaigns; the latter campaigns facilitate the translation of existing evidence into best practices for providers and patients.

Clinical decision-making tools for exam selection, reporting and dose tracking.

Although many efforts have been made to reduce the radiation dose associated with individual medical imaging examinations to "as low as reasonably achievable," efforts to ensure such examinations are performed only when medically indicated and appropriate are equally if not more important. Variations in the use of ionizing radiation for medical imaging are concerning, regardless of whether they occur on a local, regional or national basis. Such variations among practices can be reduced with the use of decision support tools at the time of order entry. These tools help reduce radiation exposure among practices through the appropriate use of medical imaging. Similarly, adoption of best practices among imaging facilities can be promoted through tracking the radiation exposure among imaging patients. Practices can benchmark their aggregate radiation exposures for medical imaging through the use of dose index registries. However several variables must be considered when contemplating individual patient dose tracking. The specific dose measures and the variation among them introduced by variations in body habitus must be understood. Moreover the uncertainties in risk estimation from dose metrics related to age, gender and life expectancy must also be taken into account.

The Environmental, Social, Governance Movement and Radiology: Opportunities and Strategy.

The environmental, social, governance (ESG) movement has come to health care organizations, in part through the Biden administration's challenge to them to reduce greenhouse gas emissions by 50% by 2030 and achieve net zero emissions by 2050, in support of more robust environmental sustainability. Radiology practices should become knowledgeable about ESG concepts and look for opportunities that are meaningful and achievable to support their host organizations' ESG efforts. Examples of initiatives to support improved environmental sustainability include selecting the least energy intensive imaging method for a given diagnosis, shutting down equipment in standby mode, sourcing energy from renewable sources, and reducing waste through recycling. Optimizing imaging protocols can reduce radiation exposure to patients, energy used per examination, and the use of other resources such as iodinated contrast media, an environmental pollutant. Achieving socially equitable access to services for ethnic and racial minorities remains a challenge in the US health care system. Extending hours of operation for screening services to include nights and weekends can provide options for patients who otherwise must take time away from work with loss of income. With respect to governance, more transparency in leadership selection and greater opportunities for participation by women and racial/ethnic minorities in the leadership of professional organizations should be supported in radiology. To succeed in ESG initiatives, radiology practice leaders should consider appointing a lead person and a multifunctional team that includes broad representation from the radiology workplace. The team should work to identify opportunities that are realistic and achievable within their institutional contexts.